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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]

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]

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]

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]

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]

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]

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]

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.)

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]

Discriminative correlation filters (DCF) have significantly advanced visual target tracking. However, most DCF-based trackers suffer from various challenges such as occlusion, rotation, and background clutters. Therefore, we propose a novel visual tracking framework, which introduces a motion-aware strategy and automatic temporal regularization mechanism into the spatial-temporal regularization correlation filter (STRCF) to improve tracking stability. Specifically, the motion-aware strategy based on the optimal Kalman filter (KF) is used to estimate the possible state of the target for overcoming the instability problem in complex environments. Furthermore, a novel automatic temporal regularization mechanism is proposed to solve the problem of target drift due to overhigh temporal penalty. Compared with STRCF, our method obtains AUC gains of 5.86%, 2.60%, 3.82%, 4.95%, 3.55%, and 1.90% for the occlusion, motion blur, in-plane Rotation, out-of-plane rotation, background clutters, and scale variation attributes on the OTB-2015 datasets, respectively. Extensive experiment results on OTB-2013, DTB-70, and UAV-123 datasets have proven the effectiveness and stability of our method. [ABSTRACT FROM AUTHOR]

Ground radar stations observing specific regions of interest nowadays provide detections in the form of point-clouds. This article focuses on a framework that consists of an elliptical multitarget tracker, referred to as Principal-Axes based Kalman Filter (PAKF)-based Joint Probabilistic Data Association (JPDA) (PAKF-JPDA), to enable maritime traffic monitoring. The framework touches on two major stages, target detection and target tracking. For the former, we employed a clustering approach and for the latter, we presented a data-association-based version of the PAKF tracker with an automatic track management functionality. The framework's benefits are demonstrated when it is applied to the radar streaming in a harbor setting based on a homogeneous multisensor tracking system by comparing our results against their corresponding reference data with visualizations, including performance measures. [ABSTRACT FROM AUTHOR]

Featured Application: This paper analyzes the automatic scanning path planning method and the scanning quality influence rule for casing parts. According to the requirements for the rapid scanning and measurement of the geometric shape during the process of chemical milling of an aviation engine casing, the scanning path of the casing is planned and studied. This paper introduces the principle and method of the tracking scanner and automatic measuring system and analyzes the scanning area range, approach distance, and wide angle of the field. The casing process is modeled by applying part of the machine, obtaining a series of scanning path point and synthesizing the scanning trajectory. On this basis, the entire scanning process is divided into two alternating actions: scanning measurement and posture adjustment, and the mathematical model of the annular scanning path on the outer surface of the casing part is obtained. Adjusting the scan height was used to solve the repeated scan area problem, and the results show that the adjustment method effectively shortened the scan path's length and time. The simulation method verifies the planned finite ring-scanning path, which verifies the correctness and feasibility of the mathematical model. Through the automatic scanning reconstruction process test, the reconstruction rate of the ring scanning trajectory reaches 85%, which is 80% higher than the manual detection efficiency. [ABSTRACT FROM AUTHOR]

The core challenges to automatic full-horizon tracking are how to establish a potential local connection relationship between the horizon points, conduct accurate global diffusion in a three-dimensional space, and finally, how to form a complex horizon surface. The existing attribute-based horizon-tracking methods based on waveform similarity, dip guidance, and RGT (relative geological time) can not solve the problems of local connection and global diffusion at the same time. In view of this challenge, this paper proposes an automatic 3D seismic horizon-tracking method based on global corrugated diffusion, which can completely integrate local connection and global diffusion so that all horizons in the whole data volume can be interpreted simultaneously. For the problem of local horizon-point connection, this paper uses the correlation between seismic trace pairs based on DTW (dynamic time warping) correlation to mine the connection mode between horizon points. For the global diffusion problem, this paper proposes the realization of global modeling based on the relationship between seismic samples, constructing a complex 3D horizon through a central ripple-diffusion process. The example shows that the horizon tracked by this method well reflects the original stratum occurrence and stratum-contact relationship, retains the structural details, accurately reflects the structural shape, and realizes automatic tracking across faults. [ABSTRACT FROM AUTHOR]

The high rate of drug withdrawal from the market due to cardiovascular toxicity or lack of efficacy, the economic burden, and extremely long time before a compound reaches the market, have increased the relevance of human in vitro models like human (patient‐derived) pluripotent stem cell (hPSC)‐derived engineered heart tissues (EHTs) for the evaluation of the efficacy and toxicity of compounds at the early phase in the drug development pipeline. Consequently, the EHT contractile properties are highly relevant parameters for the analysis of cardiotoxicity, disease phenotype, and longitudinal measurements of cardiac function over time. In this study, we developed and validated the software HAARTA (Highly Accurate, Automatic and Robust Tracking Algorithm), which automatically analyzes contractile properties of EHTs by segmenting and tracking brightfield videos, using deep learning and template matching with sub‐pixel precision. We demonstrate the robustness, accuracy, and computational efficiency of the software by comparing it to the state‐of‐the‐art method (MUSCLEMOTION), and by testing it with a data set of EHTs from three different hPSC lines. HAARTA will facilitate standardized analysis of contractile properties of EHTs, which will be beneficial for in vitro drug screening and longitudinal measurements of cardiac function. [ABSTRACT FROM AUTHOR]

The welding seam tracking operation ensures that the welding torch of the welding robot can go with the welding seam during the whole symmetrical robotic welding procession. To achieve three-dimensional complex welding seams tracking, a four-step welding seam tracking system is suggested based on segmented scanning, combined filtering, feature-point extraction, and welding path planning. From using the laser sensor installed at the end of welding robot, the welding seam data was continuously collected in multiple segments by segmented scanning. For the purpose of improving seam tracking accuracy, a combined filtering technique was used to correct the data to reduce the effects of burrs, data distortion, and noise on the surface of the weldment. Then, the feature points were collected so that the coordinate system will be calibrated to identify the welding points. Finally, a spatial welding path was obtained by welding path planning. Experimental investigations of the two-dimensional (2D) symmetrical S-shaped and three-dimensional (3D) curved welding seams were conducted. The obtained results demonstrate the proposed method can form a complete welding path. The average errors of the two weldments are about 0.296 mm and 0.292 mm, respectively. This shows that the proposed tracking method is effective and can provide a reference for the research of high-precision seam tracking and automatic welding. [ABSTRACT FROM AUTHOR]

The timely identification of differential settlement of track foundations is of great significance for the safety of train operation and the maintenance of track structures. However, traditional monitoring techniques cannot meet the requirements of efficient, real-time, and automatic monitoring of track foundation settlement. In order to solve these problems, a real-time identification method based on a gated recurrent unit (GRU) neural network is proposed for the differential settlement of track foundations monitoring. According to parameter sensitivity analysis, the vertical acceleration of the vehicle is selected as the known data fed into the GRU network for differential settlement identification. Then the GRU network is employed to establish the nonlinear relationship between the vertical acceleration of the vehicle and the differential settlement of the track foundation. The results indicate that the longitudinal continuous differential settlement distribution curve of track foundations could be accurately identified with GRU neural network through the real-time vibration response of the vehicle–track. The current method may provide a new means for the real-time and efficient identification of the differential settlement of track foundations. [ABSTRACT FROM AUTHOR]

This paper considers the trajectory tracking problem for a class of automatic straddle carriers (ASCs) with external interferences and the overturning constraints. First, the steering and the dynamics of the ASC are analyzed and the mathematical model of the ASC is established. Then, considering the impact of external interferences on the trajectory tracking accuracy, an improved dynamic Kalman filter is designed in order to obtain the state estimation of the ASC. Based on the obtained state estimation, a model-predictive control method is proposed which takes the anti-overturning constraints into account. In addition, the improved Kalman-filter-based model-predictive control (iKFMPC) algorithm is summarized for the considered ASC to travel smoothly along the given trajectory while meeting the overturning resistance. Finally, simulation analyses demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Solar energy is the cleanest and most abundant form of energy that can be obtained from the Sun. Solar panels convert this energy to generate solar power, which can be used for various electrical purposes, particularly in rural areas. Maximum solar power can be generated only when the Sun is perpendicular to the panel, which can be achieved only for a few hours when using a fixed solar panel system, hence the development of an automatic solar tracking system. Over the years, different solar tracking systems have been proposed and developed, and a few have been reviewed in the literature. However, the existing review works have not adequately provided a comprehensive survey and taxonomies of these solar tracking systems to show the trends and possible further research direction. This paper aims to bridge these gaps by extensively reviewing these time-based solar tracking systems based on axis rotation and drive types. Lessons learned from the comprehensive review have been highlighted and discussed. Finally, critical open research issues are identified and elaborated. [ABSTRACT FROM AUTHOR]

Automatic berthing is considered one of the most challenging problems for underactuated unmanned surface vehicles (USVs) due to the influence factors such as rudder effect, environmental disturbances, and control uncertainties. This paper focuses on trajectory planning and tracking to address this problem. The hybrid approach initially employs a reverse search strategy to plan the global berthing trajectory, with the terminal part of this trajectory subsequently being reconstructed and optimized by taking into account environmental loads and USV dynamic model. A nonlinear MPC controller is designed for real-time tracking of the berthing trajectory. The rudder effect constraints related to USV speed is supplemented to the dynamic model to alleviate the problem of poor maneuverability at low speed. The whole trajectory planning and tracking scheme of automatic berthing is simulated at Dalian Port considering environmental loads and uncertainties in control. The results show that the scheme has quite fast solving speed of trajectory planning and robust control of trajectory tracking. • OCP method optimizes terminal berthing for USV, improving offline planning speed. • Clarifies USV rudder effect, adds constraints for better low-speed maneuvering. • Designs robust nonlinear MPC controller for USV berthing under uncertainty. [ABSTRACT FROM AUTHOR]

How does the brain track and process rapidly changing sensory information? Current computational accounts suggest that our sensations and decisions arise from the intricate interplay between bottom-up sensory signals and constantly changing expectations regarding the statistics of the surrounding world. A significant focus of recent research is determining which statistical properties are tracked by the brain as it monitors the rapid progression of sensory information. Here, by combining EEG (three experiments N ≥ 22 each) and computational modelling, we examined how the brain processes rapid and stochastic sound sequences that simulate key aspects of dynamic sensory environments. Passively listening participants were exposed to structured tone-pip arrangements that contained transitions between a range of stochastic patterns. Predictions were guided by a Bayesian predictive inference model. We demonstrate that listeners automatically track the statistics of unfolding sounds, even when these are irrelevant to behaviour. Transitions between sequence patterns drove a shift in the sustained EEG response. This was observed to a range of distributional statistics, and even in situations where behavioural detection of these transitions was at floor. These observations suggest that the modulation of the EEG sustained response reflects a process of belief updating within the brain. By establishing a connection between the outputs of the computational model and the observed brain responses, we demonstrate that the dynamics of these transition-related responses align with the tracking of "precision" – the confidence or reliability assigned to a predicted sensory signal - shedding light on the intricate interplay between the brain's statistical tracking mechanisms and its response dynamics. • A new paradigm was used to study automatic brain tracking of stochastic sound sequences. • Sound sequences contained transitions between patterns with different distributional statistics. • A Bayesian ideal-observer model guided interpretation of EEG responses. • EEG responses to stochastic patterns were closely associated with precision tracking. [ABSTRACT FROM AUTHOR]

Visual recognition is easily affected by broken ground identification, reflection of strong light and narrow field of view, while GPS (global positioning system) location is susceptible to occlusion and electromagnetic interference. Using any single positioning method will cause the problem of low tracking availability. Therefore, a fault-tolerant control method of combined vision and GPS combined inertial navigation is proposed in this paper. Firstly, lane line recognition is carried out based on image recognition method, geometric relationship parameters between vehicle and road are obtained, and PID controller is designed for steering control. Secondly, layered electronic map is constructed by RTK-GPS (real-time kinematic GPS), smooth reference track is output, and then Stanley controller is designed for steering control. Finally, vision and GPS tracking control are fused. Through real vehicle verification, the results show that the proposed fusion control strategy effectively improves the small radius turning passing rate of autonomous-rail rapid tram, the maximum passing speed can reach 25 km/h, which is increased by 66.7%, the number of one-way takeovers is reduced by 83.8%, the operation time is reduced by 24.4%, and the system availability is effectively improved.

Background: Automatic cell tracking methods enable practitioners to analyze cell behaviors efficiently. Notwithstanding the continuous development of relevant software, user-friendly visualization tools have room for further improvements. Typical visualization mostly comes with main cell tracking tools as a simple plug-in, or relies on specific software/platforms. Although some tools are standalone, limited visual interactivity is provided, or otherwise cell tracking outputs are partially visualized. Results: This paper proposes a self-reliant visualization system, CellTrackVis, to support quick and easy analysis of cell behaviors. Interconnected views help users discover meaningful patterns of cell motions and divisions in common web browsers. Specifically, cell trajectory, lineage, and quantified information are respectively visualized in a coordinated interface. In particular, immediate interactions among modules enable the study of cell tracking outputs to be more effective, and also each component is highly customizable for various biological tasks. Conclusions: CellTrackVis is a standalone browser-based visualization tool. Source codes and data sets are freely available at http://github.com/scbeom/celltrackvis with the tutorial at http://scbeom.github.io/ctv_tutorial. [ABSTRACT FROM AUTHOR]

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The aims of the study were to analyze metabolic power (MP) and MP derived parameters for different positions in the German Soccer Bundesliga and to evaluate if classification of high-intensity is more suited using the metabolic power approach instead of using traditional speed-based methods. 1,345 video match analysis (25 Hz) datasets from 380 players in 96 matches of the German first Bundesliga were gathered by an automatic player tracking system. Displacement (speed, acceleration, distance) and energetic (MP, energy expenditure) variables were determined. Intensity was classified utilizing conventional thresholds. Metabolic and running profiles were compared among six positional groups and between the halves of the match respectively (one-way ANOVA). Further, time spent, distance covered and energy expended at high speed (>15.5 km h-1 ) and high acceleration (>2ms−2 ) were compared to those at high MP (>20 W kg-1 ) (one-way ANOVA) for evaluating if metabolic power is more suited to describe intensity in team sports. Main findings are that central-attacking midfielders (CAM) and central midfielders (CM) expended more energy (CAM: 59.8 ± 4.2 kJ kg-1 ; CM: 59.6 ± 3.6 kJ kg-1 ) and covered more distance (CAM: 11,494 ± 765 m; CM: 11,445 ± 638 m) than all other positions (p < 0.001). In the whole group of players, less time (t) and less energy were expended (EE) in high speed (t: 302 ± 84 s; EE: 10.1 ± 2.9 kJ kg-1 ) and at high acceleration zones (t: 147 ± 24 s; EE: 5.5 ± 1.2 kJ kg-1 ) when compared to high MP zone (t: 617 ± 141 s; EE: 20.0 ± 4.4 kJ kg-1 ) (p < 0.001). Furthermore, players covered more distance at high MP (2,464 ± 597 m) than at high speed (1,638 ± 458 m) and much less at high acceleration (359 ± 67 m) (p < 0.001). The higher activity profiles of CAM and CM compared to the other positions indicate the need for higher developed physiological performance in players of these positions. High intensity activities should be interpreted differently when using MP and displacement parameters as indicators of high intensity in soccer. [ABSTRACT FROM AUTHOR]

The solar stills can solve the problem of freshwater shortage for the people living in remote areas. Through this work, the performance of hybrid solar desalination arrangement was investigated. The system comprised of parabolic trough solar collector, automatic solar tracking system, separation room, modified solar still, two condensation units, feed water tank, and supplementary and measuring tools. The effect of using various water flow rates on the effectiveness of parabolic trough solar collector was tested. Also, the influences of integrating condenser to the modified solar still with and without phase change material were studied. The experimental results revealed that the best flow rate for parabolic trough solar collector was obtained as 10 L/h (100 L/day), where its effectivity was 61%. Under this condition, the obtained freshwater was around 61 L/day from a total saline water of 100 L/day. In addition, integrating a condenser to the modified solar still improved its productivity. So, the total yields of conventional and modified solar stills were 2500 and 5145 mL/m2.day, respectively. Therefore, the distillate of modified solar still was improved by 105.8% as compared to that of the conventional solar still due to using the hot feed water and external condenser. Moreover, using phase change material enhanced the modified solar still yield. The total yield of conventional solar still and modified solar still with hot feed water, condenser, and phase change material was 2575 and 6150 mL/m2.day, respectively. Consequently, the productivity of the modified solar still with hot feed water, condenser, and phase change material was improved by around 138.83% over that of the conventional solar still. Finally, the conventional solar still had an average efficiency of 31.5%. Also, when using the hot feed water and external condenser with the modified solar still, the thermal efficiency was reported as 49.7%. Also, the modified solar still with hot water, condenser, and phase change material had a thermal efficiency of 56.5%. Finally, the water costs of 1 L from the CSS and MSS are 0.063 $ and 0.050 $, respectively. [ABSTRACT FROM AUTHOR]

Daphnia magna is an important organism in ecotoxicity studies because it is sensitive to toxic substances and easy to culture in laboratory conditions. Its locomotory responses as a biomarker are highlighted in many studies. Over the last several years, multiple high-throughput video tracking systems have been developed to measure the locomotory responses of Daphnia magna. These high-throughput systems, used for high-speed analysis of multiple organisms, are essential for efficiently testing ecotoxicity. However, existing systems are lacking in speed and accuracy. Specifically, speed is affected in the biomarker detection stage. This study aimed to develop a faster and better high-throughput video tracking system using machine learning methods. The video tracking system consisted of a constant temperature module, natural pseudo-light, multi-flow cell, and an imaging camera for recording videos. To measure Daphnia magna movements, we developed a tracking algorithm for automatic background subtraction using k-means clustering, Daphnia classification using machine learning methods (random forest and support vector machine), and tracking each Daphnia magna location using the simple online real-time tracking algorithm. The proposed tracking system with random forest performed the best in terms of identification (ID) precision, ID recall, ID F1 measure, and ID switches, with scores of 79.64%, 80.63%, 78.73%, and 16, respectively. Moreover, it was faster than existing tracking systems such as Lolitrack and Ctrax. We conducted an experiment to observe the impact of toxicants on behavioral responses. Toxicity was measured manually in the laboratory and automatically using the high-throughput video tracking system. The median effective concentration of Potassium dichromate measured in the laboratory and using the device was 1.519 and 1.414, respectively. Both measurements conformed to the guideline provided by the Environmental Protection Agency of the United States; therefore, our method can be used for water quality monitoring. Finally, we observed Daphnia magna behavioral responses in different concentrations after 0, 12, 18, and 24 h and found that there was a difference in movement according to the concentration at all hours. [ABSTRACT FROM AUTHOR]

Four-wheeled, individual-driven, nonholonomic structured mobile robots are widely used in industries for automated work, inspection and exploration purposes. The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure. The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots. However, there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis's motion. As a result, the mobile robot has limited performance, such as chattering during curved movement. In this research work, a three-tiered adaptive robust control with fuzzy parameter estimation, including dynamic modeling, direct torque control and wheel slip control is proposed. Fuzzy logic-based parameter estimation is a valuable tool for adjusting adaptive robust controller (ARC) parameters and tracking the trajectories with less tracking error as well as high tracking accuracy. This research considers the O type and 8 type trajectories for performance analysis of the proposed novel control technique. Our suggested approach outperforms the existing control methods such as Fuzzy, proportional-integral-derivative (PID) and adaptive robust controller with discrete projection (ARC-DP). The experimental results show that the scheduled performance index decreases by 2.77% and 4.76%. All the experimental simulations obviously proved that the proposed ARC-Fuzzy performed well in smooth groud surfaces compared to other approaches. [ABSTRACT FROM AUTHOR]

Position tracking modeling and automatic control of an innovative anchor beam supporting robot are presented. Specifically, the forward and inverse kinematics model integrated with BP neural network and the mechanical-hydraulic co-simulation model based on ante-valve pressure compensation method are developed and applied. The experimental system is built to verify the models and method. The results show that position tracking precision can reach the sub-centimeter level. The influence of variable load on hydraulic flow control is weakened effectively and the stability and controllability of load sensitive system is improved. The developed model and control approach are valuable for virtual development and automated testing during the commissioning of hydraulic manipulator. [ABSTRACT FROM AUTHOR]

In existing surgery process, surgeons need to manually adjust the laparoscopes to provide a better field of view (FOV) during operation, which may distract surgeons and slow down the surgery process. Herein, a data‐driven control method that uses a continuum laparoscope to adjust the FOV by tracking the surgical instruments is presented. A Koopman‐based system identification method is first applied to linearize the nonlinear system. Shifted Chebyshev polynomials are used to construct observation functions that transfer low‐dimension observations to high‐dimension ones. The Koopman operator is approximated using a finite‐dimensional estimation method. An optimal controller is further developed according to the trained linear dynamic model. Furthermore, a learning‐based pose estimation framework is designed to detect keypoints on surgical instruments and provides visual feedback for the control system. Compared with other detection methods, the proposed scheme achieves a higher detection precision and provides more optional keypoints for tracking. Simulation and experiments validate the feasibility of the proposed control method. Experimental results show that the proposed method can automatically adjust the field of continuum laparoscope by tracking surgical instruments in real time and satisfy the clinical requirements. [ABSTRACT FROM AUTHOR]

The ERA5 new generation of high-resolution reanalysis provides a possibility to obtain more accurate cyclone tracks in the Southern Ocean. With a commonly used cyclone tracking algorithm, this study evaluates the impact of data resolution on Southern Ocean cyclone tracks for the period from 1980 to 2020 by preprocessing the ERA5 dataset at different spatial and temporal resolutions. A new track-matching method is proposed to assure an accurate comparison of different track datasets, considering the multiple match pairs and best match pair for each track. It is found that the number, distribution, and characteristics of cyclones are considerably different for various resolution scenarios. The higher spatial resolution captures more tracks, while the increased temporal resolution decreases the number, as well as the lifetime and the moving distance of tracks. The shared cyclones of different track datasets show different characteristics, influenced by both spatial and temporal resolutions. Higher spatial resolution schemes tend to identify more additional track points after the overlapping time of shared tracks rather than before. The spatial distribution pattern of additional track points is consistent when increasing temporal or spatial resolution separately. These results are a reference for the application of objective tracking algorithms in the Southern Ocean using input data with higher resolution. Significance Statement: Automatic tracking algorithms are important tools for the research of cyclones and their associated weather phenomena. High-resolution input data are available now for cyclone tracking but it also brings more noise that may affect the results. The problem can be solved by a preprocess (smoothing) process using suitable spatial and temporal resolutions. This study compared the features of Southern Ocean cyclones obtained by different spatial and temporal resolution schemes. We further study the shared cyclones in different track datasets with a new track-matching method and discuss the different impacts of spatial and temporal resolution, as well as provide a reference for the application and improvement of cyclone tracking in the future. [ABSTRACT FROM AUTHOR]

Visual tracking is an essential process for performing automated cell manipulation tasks. However, Real-time accurate cell tracking in FluidFM is challenging due to probe occlusion during manipulation. This study presents a correlation filtering-based tracking method for real-time, high-precision position tracking of cells and probes. In this method, a target template update strategy is proposed to realize cell tracking under probe occlusion, which abandons the use of pixel values in the occlusion area and reduces the interference of the occlusion area in model learning. Cell tracking and automated manipulation experiments were carried out to validate the proposed method. The Experimental results show that the proposed tracking method can accurately measure the positions of occlusion cells and probe. Furthermore, combined with the precise force control of the FluidFM's probe, the automated cell Pick & Place experiments was accomplished with high accuracy and robustness. [ABSTRACT FROM AUTHOR]

Effective livestock management is critical for cattle farms in today's competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are not feasible in today's farming systems. Fortunately, automatic tracking and identification systems have greatly improved in recent years. Moreover, correctly identifying individual cows is an integral part of predicting behavior during estrus. By doing so, we can monitor a cow's behavior, and pinpoint the right time for artificial insemination. However, most previous techniques have relied on direct observation, increasing the human workload. To overcome this problem, this paper proposes the use of state-of-the-art deep learning-based Multi-Object Tracking (MOT) algorithms for a complete system that can automatically and continuously detect and track cattle using an RGB camera. This study compares state-of-the-art MOTs, such as Deep-SORT, Strong-SORT, and customized light-weight tracking algorithms. To improve the tracking accuracy of these deep learning methods, this paper presents an enhanced re-identification approach for a black cattle dataset in Strong-SORT. For evaluating MOT by detection, the system used the YOLO v5 and v7, as a comparison with the instance segmentation model Detectron-2, to detect and classify the cattle. The high cattle-tracking accuracy with a Multi-Object Tracking Accuracy (MOTA) was 96.88%. Using these methods, the findings demonstrate a highly accurate and robust cattle tracking system, which can be applied to innovative monitoring systems for agricultural applications. The effectiveness and efficiency of the proposed system were demonstrated by analyzing a sample of video footage. The proposed method was developed to balance the trade-off between costs and management, thereby improving the productivity and profitability of dairy farms; however, this method can be adapted to other domestic species. [ABSTRACT FROM AUTHOR]

Pest management using attractive and/or repellent semiochemicals is a key alternative to synthetic insecticides. Its implementation requires a good understanding of the intra‐ and interspecific chemical interactions of arthropod pests, their interactions with their abiotic environment, as well as their evolutionary dynamics. Although mites include many pest species and biocontrol agents of economic importance in agriculture, their chemical ecology is largely understudied compared to insects. We developed a high‐throughput ethomics system to analyze these small arthropods and conducted a study on Dermanyssus gallinae, a problematic poultry parasite in the egg industry. Our purpose was to elucidate the role played by host‐derived odorants (synthetic kairomone) and conspecific odorants (mite body odors) in D. gallinae. After validating our nanocomputer controlled olfactometric system with volatile semiochemicals of known biological activity, we characterized response traits to kairomonal and/or pheromonal volatile blends in mites from different populations. We were able to accurately characterize the repulsion or attraction behaviors in >1000 individual specimens in a standardized way. Our results confirm the presence of a volatile aggregation pheromone emitted by D. gallinae and bring new elements to the effect of odor source presentation. Our results also confirm the attractive effect on Dermanyssus gallinae of a blend of volatile compounds contained in hen odor, while highlighting a repellent effect at high concentration. Significant interindividual and interpopulation variation was noted particularly in responses to synthetic kairomone. This information lays a valuable foundation for further exploring the emergence risk of resistance to semiochemicals. RESEARCH HIGHLIGHTS: Through a new high‐throughput system based on nanocomputers connected to cameras automatically tracking movements, we highlight behavioral responses of a hematophagous mite species to kairomones and pheromones. [ABSTRACT FROM AUTHOR]

In this article, an automatic tracking process for unmanned aerial vehicle (UAV) system to follow the target with maneuverability is developed, and a vision‐based control method is proposed. At first, the image‐based visual servoing (IBVS) position control technology is applied without any prior target information for the algorithm practicability. Besides, consider the situation with unavailable angular velocity measurement, a thorough nonlinear attitude controller with an observer based on contraction is proposed. Finally, the whole UAV system stability is demonstrated to be uniformly ultimately bounded, and the bound can be arbitrarily small. Simulation and experiment results are presented to show effectiveness of the designed controller. [ABSTRACT FROM AUTHOR]

The assessment of tropical cyclone (TC) statistics requires the direct, objective, and automatic detection and tracking of TCs in reanalyses and model simulations. Research groups have independently developed numerous algorithms during recent decades in order to answer that need. Today, there is a large number of trackers that aim to detect the positions of TCs in gridded datasets. The questions we ask here are the following: does the choice of tracker impact the climatology obtained? And, if it does, how should we deal with this issue? This paper compares four trackers with very different formulations in detail. We assess their performances by tracking TCs in the ERA5 reanalysis and by comparing the outcome to the IBTrACS observations database. We find typical detection rates of the trackers around 80 %. At the same time, false alarm rates (FARs) greatly vary across the four trackers and can sometimes exceed the number of genuine cyclones detected. Based on the finding that many of these false alarms (FAs) are extra-tropical cyclones (ETCs), we adapt two existing filtering methods common to all trackers. Both post-treatments dramatically impact FARs, which range from 9 % to 36 % in our final catalogs of TC tracks. We then show that different traditional metrics can be very sensitive to the particular choice of tracker, which is particularly true for the TC frequencies and their durations. By contrast, all trackers identify a robust negative bias in ERA5 TC intensities, a result already noted in previous studies. We conclude by advising against using as many trackers as possible and averaging the results. A more efficient approach would involve selecting one or a few trackers with well-known and complementary properties. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) pandemic has triggered a huge transformation in the use of existing technologies. Many innovations have been made in the field of contact tracing and tracking. However, studies have shown that there is no holistic system that integrates the overall process from data collection to the proper analysis of the data and actions corresponding to the results. It is critical to identify any contact with infected people and to ensure that they do not interact with others. In this research, we propose an IoT-based system that provides automatic tracking and contact tracing of people using radio frequency identification (RFID) and a global positioning system (GPS)-enabled wristband. Additionally, the proposed system defines virtual boundaries for individuals using geofencing technology to effectively monitor and keep track of infected people. Furthermore, the developed system offers robust and modular data collection, authentication through a fingerprint scanner, and real-time database management, and it communicates the health status of the individuals to appropriate authorities. The validation results prove that the proposed system identifies infected people and curbs the spread of the virus inside organizations and workplaces. [ABSTRACT FROM AUTHOR]

When browsing or studying a video corpus, particularly relevant information consists in knowing who are the people appearing in the scenes. In this paper, we show how a combination of state of the art techniques can be organised in a pipeline for face recognition of celebrities. In particular, we propose a system which combines MTCNN for detecting faces and FaceNet for extracting face embeddings, which are used to train a set of classifiers. The face recognition results obtained at a frame level are then combined with those in consecutive frames, relying on automatic object tracking. Differently from previous work, we use images automatically retrieved by web search engines. We evaluate the systems one three datasets including historical videos from 1945 to 1969 and contemporary videos, obtaining a good precision score. In addition, we show how the obtained results can be applied to foster historical studies. [ABSTRACT FROM AUTHOR]