Merging Point Research Articles

Research on the propagation characteristics of multiple cracks in steel bridge joints

This study explores the morphological changes and interaction mechanisms of multiple crack propagations in steel bridge joints. Fatigue testing was conducted to determine the locations at which multiple cracks initiated and to obtain fatigue fracture surfaces with easily discernible crack propagation traces. Based on the theory of fracture mechanics, the propagation behavior of coplanar and out-of-plane cracks in joints was simulated using ABAQUS and FRANC3D joint simulation technology. The characteristics of the morphological changes were analyzed, and the interaction mechanism between different crack spacings was investigated to quantitatively characterize the relative positions at which cracks were enhanced or suppressed. The results showed that coplanar cracks exhibited different morphological trends and propagation rates in different stages of propagation. The shape of the crack front continuously changed, and the merging point that appeared during fusion rapidly expanded and evolved into a semi-elliptical crack. The fatigue life was more sensitive to the relative position of multiple cracks. In particular, the fusion of coplanar cracks significantly reduced the fatigue life, and the mutual suppression effect between heterogeneous cracks increased the fatigue life. When the ratio of coplanar crack spacing to crack length (s/2c) was less than 0.2, the crack fusion process significantly accelerated. In addition, when the a2/a1 ratio of the crack propagation depth dropped below 0.5, the suppression effect was significant. Finally, by determining the spacing between multiple cracks, the method established this study can provide a reference for the design of fatigue resistance and joint optimization.

Game-Based Flexible Merging Decision Method for Mixed Traffic of Connected Autonomous Vehicles and Manual Driving Vehicles on Urban Freeways

Connected Autonomous Vehicles (CAVs) have the potential to revolutionize traffic systems by autonomously handling complex maneuvers such as freeway ramp merging. However, the unpredictability of manual-driven vehicles (MDVs) poses a significant challenge. This study introduces a novel decision-making approach that incorporates the uncertainty of MDVs’ driving styles, aiming to enhance merging efficiency and safety. By framing the CAV-MDV interaction as an incomplete information static game, we categorize MDVs’ behaviors using a Gaussian Mixture Model–Support Vector Machine (GMM-SVM) method. The identified driving styles are then integrated into the flexible merging decision process, leveraging the concept of pure-strategy Nash equilibrium to determine optimal merging points and timing. A deep reinforcement learning algorithm is employed to refine CAVs’ control decisions, ensuring efficient right-of-way acquisition. Simulations at both micro and macro levels validate the method’s effectiveness, demonstrating improved merging success rates and overall traffic efficiency without compromising safety. The research contributes to the field by offering a sophisticated merging strategy that respects real-world driving behavior complexity, with potential for practical applications in urban traffic scenarios.

Four-Dimensional Trajectory Optimization for CO2 Emission Benchmarking of Arrival Traffic Flow with Point Merge Topology

Four-Dimensional Trajectory Optimization for CO2 Emission Benchmarking of Arrival Traffic Flow with Point Merge Topology

Research on a Train Safety Driving Method Based on Fusion of an Incremental Clustering Algorithm and Lightweight Shared Convolution.

This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as a core method, combined with dilation and erosion theories, to expand the boundaries of point cloud clusters, merging adjacent point cloud elements into unified clusters. This method effectively identifies and connects spatially adjacent point cloud clusters while efficiently eliminating noise from target object point clouds, thereby achieving more precise recognition of unknown obstacles on the track. Furthermore, the effective integration of this algorithm with lightweight shared convolutional semantic segmentation algorithms enables accurate localization of obstacles. Experimental results using two combined public datasets demonstrate that the obstacle detection average recall rate of the proposed method reaches 90.3%, significantly enhancing system reliability. These findings indicate that the proposed detection strategy effectively improves the accuracy and real-time performance of obstacle recognition, thereby presenting important practical application value for ensuring the safe operation of railway tracks.

Underwater Mapping and Optimization Based on Multibeam Echo Sounders

Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration.

Modelling of opposed flame spread over thin electric wires: estimation of flame spread rates and limiting oxygen concentrations

A model of opposed flame spread along thin electric wires is developed to estimate the variation of flame spread rate ( V f ) with oxygen concentrations and oxidiser flow velocity. The length of pyrolysis zone ( L py ) and V f are treated as eigenvalues of the problem, and one-dimensional energy balance equations for the wire core and insulation are formulated to derive their solutions. To obtain a set of steady solutions for L py and V f , the macroscopic energy balance equation for the entire system is calculated from the one-dimensional energy balance equations with appropriate boundary conditions. Since the equations are nonlinear function of L py and V f , multiple solutions appear. The physical meaning of each solution is evaluated based on the deviation of energy from the equilibrium. Comparison of the temperature distribution along the wire with experimental data confirms that the model reproduces well the actual flame spread process. The variation of V f with oxygen concentration shows a C-shaped curve. The upper branch is the stable solution, and the lower branch is the unstable solution and the merging point of two branches correspond to the limit condition for flame spread. Therefore, the model can be used to estimate the limiting oxygen concentration based on the existence of a solution for V f . Furthermore, the variation of V f with oxidiser flow velocity shows O-shaped curve, and it is confirmed that radiative extinction under low flow velocity and blow-off under high flow velocity can also be estimated with the model. Mapping of variations of radiative extinction and blow-off extinction with oxygen concentration reveals the U-shaped flammability diagram. The predicted flame spread limits are in good agreement with the limiting oxygen concentrations measured in both microgravity and normal gravity, demonstrating the validity of using this model to predict flammability characteristics of electric wires under various oxidiser flow velocity.

Two-dimensional hydrodynamic simulation for synchronized oscillatory flows in two collapsible channels connected in parallel.

We investigated self-sustained oscillation in a collapsible channel, in which a part of one rigid wall is replaced by a thin elastic wall, and synchronization phenomena in the two channels connected in parallel. We performed a two-dimensional hydrodynamic simulation in a pair of collapsible channels which merged into a single channel downstream. The stable synchronization modes depended on the distance between the deformable region and the merging point; only an in-phase mode was stable for the large distance, in-phase and antiphase modes were bistable for the middle distance, and again only an in-phase mode was stable for the small distance. An antiphase mode became stable through the subcritical pitchfork bifurcation by decreasing the distance. Further decreasing the distance, the antiphase mode became unstable through the subcritical Neimark-Sacker bifurcation. We also clarified the distance dependences of the amplitude and frequency for each stable synchronization mode.

Andreev reflections in deformed semi-Dirac material superconducting junctions

Besides the strong anisotropy, another unique feature of the semi-Dirac materials (SDMs) is the evolution of its dispersion with gap parameter from a merging semi-Dirac point to two Dirac points. In this study, we study the superconducting coherent transport at the SDM superconducting junction and focus on the influence of evolution of the SDM dispersion on the Andreev reflection. We found that the coexistence of peculiar quadruple reflections, including double specular normal reflection and double retro Andreev reflection, can occur within a wide range of parameters. We in detail discuss the dependence of reflection probabilities on the incident angle, incident energy, and gap parameter. By modulating these parameters, we also observe rich double reflection scenarios. These results enrich our understanding of the superconducting coherent transport and facilitate the probing of the intrinsic gap parameter in SDMs experimentally.

An enhanced motion planning approach by integrating driving heterogeneity and long-term trajectory prediction for automated driving systems: A highway merging case study

Navigating automated driving systems (ADSs) through complex driving environments is difficult. Predicting the driving behavior of surrounding human-driven vehicles (HDVs) is a critical component of an ADS. This paper proposes an enhanced motion-planning approach for an ADS in a highway-merging scenario. This method utilizes the results of two aspects: the driving behavior and long-term trajectory of surrounding HDVs, which are coupled using a hierarchical model that is used for the motion planning of an ADS to improve driving safety. An unsupervised clustering algorithm is utilized to classify HDV drivers into two categories: aggressive and normal, as part of predicting their driving behaviors. Subsequently, a logistic regression model is employed for driving style prediction. For trajectory prediction, a transformer-based model that concentrated parallelization computations on longer sequence predictions via a self-attention mechanism is developed. Based on the predicted driving styles and trajectories of the surrounding HDVs, an intelligent decision-making strategy is utilized for ADS motion planning. Finally, real-world traffic data collected from drones on a highway ramp in Xi’an is used as a case study to train and evaluate the proposed model. The results demonstrate that the proposed approach can predict HDVs merging trajectories with a mean squared error (MSE) smaller than 0.125 at 30 s away from the merging point, outperforming existing approaches in terms of predictable duration and accuracy. Furthermore, it exhibited safety improvements by adjusting the ADS motion state in advance with good predictive power for the surrounding HDVs’ motion. For both normal and aggressive driving styles, the ADS trajectories reached optimal safety at a prediction horizon of at least 10 s and over 6.67 s, respectively, when evaluating both time-to-collision (TTC) and deceleration rate to avoid a crash (DRAC) metrics. This indicates that our proposed procedures have improved safety for AVs with long-term predictive capabilities (i.e., over 6 s) for surrounding HDVs. Interestingly, the safety performance does not continue to improve with the increase in prediction horizon once the optimal safety performance (highest TTC and lowest DRAC) has been achieved for both normal and aggressive styles. This suggests that an excessively long prediction horizon (e.g., 30 s) is not always beneficial for safety performance of ADS in our case study. When ADS encounter aggressive HDVs, having a predicted surrounding HDV trajectory can extremely reduce crash risk and maintain a safe situation. This demonstrates that planning ADS trajectories with HDV predictions has significant effects on safety, especially for aggressive driving styles as compared to normal driving styles.

An Experimental Anatomic CBCT Study on the Correlations Between MB1 and MB2 of the Mesio-Vestibular Root of the Upper First Molars.

This experimental anatomic study aimed to investigate the correlations between the mesio-vestibular canal (MB1) and the second mesio-vestibular canal (MB2) of the mesio-vestibular root of the upper sexts using cone-beam computed tomography (CBCT) imaging. A total of 24 extracted maxillary first molars were collected and subjected to CBCT imaging. The presence, location, and morphology of MB1 and MB2 canals were evaluated using axial, coronal, and sagittal CBCT sections. The interrelation between MB1 and MB2 canals was assessed, including their separate canals, merging, and division points. Among the 24 maxillary first molars examined, 86.5% demonstrated the presence of an MB2 canal in addition to MB1. The MB2 and MB1 were confluent in 80% of the cases with a confluent height of 4,16mm. The mean distance between MB1 and MB2 canals was 1.85mm. This study provides detailed information on the anatomic correlations between MB1 and MB2 canals of the mesio-vestibular root in the upper sexts using CBCT imaging. The high prevalence of MB2 canals suggests their significance in endodontic procedures and emphasizes the importance of thorough exploration and identification during root canal treatment. The knowledge of the location and morphology of these canals can aid in successful endodontic therapy and enhance treatment outcomes.

Efficient shrub modelling based on terrestrial laser scanning (TLS) point clouds

ABSTRACT This paper presents a methodology for automatically generating 3D models of individual shrubs from point clouds acquired by Terrestrial Laser Scanning (TLS) devices. The creation of accurate 3D models for shrubs holds significant value for ecological studies. The shrub modelling is more challenging compared to other tree modelling tasks, due to the presence of substantial noise and fuzzy branch structures. To address these challenges, our approach first partitions the input point cloud into two categories: branches and others. This point segmentation process effectively eliminates interference of leaves and noise, thereby enhancing the visibility of branch skeletons. Then, we construct a triangulation network using the branch points and establish a minimum spanning tree (MST) based on this network. Serving as the initial skeleton representation of the plant, the MST preserves the essential topological structures of the branches. Within the extracted MST, we implement a recursive trimming technique to eliminate redundant branches by merging adjacent points and edges, ultimately consolidating the skeleton structure. Finally, we employ an adaptive cylinder fitting algorithm that relies on robust principal component analysis (RPCA) to generate the model. The effectiveness and robustness of the proposed method are demonstrated via experiments on different datasets, with an average fitting error of 1.13 cm, indicating that our approach can achieve high accuracy in modelling individual shrubs.

World literature and transformative learning

This study investigates how Tayeb Salih’s Season of Migration to the North (2006) can be read and taught as an example of world literature in accordance with the transformative and culturally empowering ambitions of the Swedish upper secondary school curriculum. A total of 18 third-year students in the upper secondary science programme read the novel and recorded their reading experiences in journals. These journals have been thematically analysed, and the results show that the students’ processes of deconstructing and reconstructing the finalised reading, using literary concepts, help forward estrangement effects, which produce critical readings. As the students read for the plot and closed in on the end, their text-centred understandings of the novel were heightened, and by actively using subject-specific terminology (i.e. stylistic devices and modes of reading concepts), they strengthened the sense-making of their relations to the world as mediated through the text. Frames of reference about historical and current Sudan support the students in allowing the novel to become a merging point, at which their cultural horizons are nuanced through the juxtaposing of different perspectives. The students’ meta-reflexive readings allow for experiencing culture on the move as part of their transformative learning.

Boundary asymptotics of non-intersecting Brownian motions: Pearcey, Airy and a transition

We study n non-intersecting Brownian motions, corresponding to the eigenvalues of an n × n Hermitian Brownian motion. At the boundary of their limit shape we find that only three universal processes can arise: the Pearcey process close to merging points, the Airy line ensemble at edges and a novel determinantal process describing the transition from the Pearcey process to the Airy line ensemble. The three cases are distinguished by a remarkably simple integral condition. Our results hold under very mild assumptions, in particular we do not require any kind of convergence of the initial configuration as n→∞ . Applications to largest eigenvalues of macro- and mesoscopic bulks and to random initial configurations are given.

A novel methodology for data analysis of dynamic angle of repose tests and powder flow classification

We present a new post-processing methodology to analyse powder flow image data gathered via dynamic angle of repose tests. We aim to expand the flow descriptors, allowing a more detailed and nuanced measurement of flow rheology. This makes the data extraction reliable even if the free surface profile is not clearly identifiable. After defining 30 flow descriptors to be measured from powder flow snapshots, we use Principal Component Analysis to understand their relations with the physics of the system. The set of descriptors is optimised, and the most significant ones are identified, allowing the physics to be captured with fewer essential parameters. We demonstrate that a comprehensive picture of powder flow is achievable simply with the centre of mass and the flow merging point. Our research demonstrates that traditional data extraction methodologies are insufficient to fully describe the flow, making our framework ideal for enhancing the understanding of flow properties.

Reviews and Responses for Investigation of Point Merge Utilization Worldwide Using Opensky Network Data

See detailed reviews and responses in the PDF file.
 DOI for the original paper: https://doi.org/10.59490/joas.2023.7218

A reinforcement learning approach to vehicle coordination for structured advanced air mobility

Advanced Air Mobility (AAM) has emerged as a pioneering concept designed to optimize the efficacy and ecological sustainability of air transportation. Its core objective is to provide highly automated air transportation services for passengers or cargo, operating at low altitudes within urban, suburban, and rural regions. AAM seeks to enhance the efficiency and environmental viability of the aviation sector by revolutionizing the way air travel is conducted. In a complex aviation environment, traffic management and control are essential technologies for safe and effective AAM operations. One of the most difficult obstacles in the envisioned AAM systems is vehicle coordination at merging points and intersections. The escalating demand for air mobility services, particularly within urban areas, poses significant complexities to the execution of such missions. In this study, we propose a novel multi-agent reinforcement learning (MARL) approach to efficiently manage high-density AAM operations in structured airspace. Our approach provides effective guidance to AAM vehicles, ensuring conflict avoidance, mitigating traffic congestion, reducing travel time, and maintaining safe separation. Specifically, intelligent learning-based algorithms are developed to provide speed guidance for each AAM vehicle, ensuring secure merging into air corridors and safe passage through intersections. To validate the effectiveness of our proposed model, we conduct training and evaluation using BlueSky, an open-source air traffic control simulation environment. Through the simulation of thousands of aircraft and the integration of real-world data, our study demonstrates the promising potential of MARL in enabling safe and efficient AAM operations. The simulation results validate the efficacy of our approach and its ability to achieve the desired outcomes.

Evaluation of Airspace Operation Safety Level based on PMS

The point merge (PM) flight procedure is a new type of flight procedure of performance -based navigation (PBN). The capacity of the point merge procedure is evaluated through the parameters of the point merge flight procedure. Through the parameters of the point merge flight procedure and the parameters of the aircraft flying in the point merge flight procedure, the collision risk between two aircraft in the merge area and the overall collision risk of the aircraft in the point merge procedure are calculated. The evaluation methods and calculation formulas of these three parameters are summarized, and a reasonable method of airspace operation safety level evaluation based on point merge system (PMS) is sought. The inferred method of airspace operation safety level evaluation based on PMS is to compare the operation capacity with the calculated theoretical capacity, and compare the collision risk between two aircraft in the fusion area calculated according to track data and radar data and the overall collision risk of aircraft in PMS with the target safety level specified by China’s air transportation industry.

Comparison of performance between PMS and trombone arrival route topologies in terminal maneuvering area

The contradiction between air traffic capacity and demand exerts congestion and delay, resulting in airspace operating at or above its capacity. This paper proposes a performance comparison of two arrival route topologies in Terminal Maneuvering Area (TMA) with a tailored optimization algorithm, including Point Merge System (PMS) and trombone paradigms. We aim at enhancing the capacity of arrival topologies and efficiently landing more aircraft through identifying which topological route is better suitable for current traffic demands. The problem involves integrating and sequencing arrival operations into the TMA. A mathematical model is developed for which entry times, entry speeds and flight routes are regarded as decision variables. The operation constraints of this optimization modelling correspond to detection of potential conflicts between consecutive aircraft at merging waypoints and on the same link segments, based on the safety separation standards. The objective function is set to minimize the delay and speed deviation for all flights in the optimized system. Because of the highly combinatorial nature of this problem and the non-deterministic polynomial hard (NP-hard), a tailored selective simulated annealing algorithm is proposed to optimally make decisions for the arrival traffic flow. Finally, the validation of our approach has been performed for Ataturk Airport in Turkey, by using 11 different operation scenarios of arrival demands. For lower demands, all aircraft can be positively scheduled, while for more than 80% extra scenarios based on the regular traffic demand, there exist some remaining conflicts for the situations. The results illustrate that, compared with the trombone procedure, PMS paradigm could enhance the arrival capacity and has better performance throughout the arrival procedure. Additionally, more flexible sequence position shift is possible in PMS, and it has the ability to accommodate high-density flight operating environment.

Quasi-stochastic optimization model for time-based arrival scheduling considering Standard Terminal Arrival (STAR) track time and a new delay-conflict relationship

This paper presents a quasi-stochastic mixed integer programming model to determine the optimal arrival schedule of flights in the Terminal Area (TMA). The proposed formulation addresses two key aspects of TMA operations: (i) the variability induced due to the Standard Terminal Arrival (STAR) descent procedure and (ii) the risk of merging conflicts due to scheduling delays. The variability arising from the STAR procedure is captured through a non-deterministic parameter defined as the track time. The risk of conflicts at merging points is quantified using the expected number of conflicts, estimated based on a new delay-conflict relationship. The optimization model is also extended for a multiple-runway structure. The Chennai International Airport TMA is taken as an experimental setup to validate the model. Performance assessment reveals that the observed throughput aligns with the throughput based on the optimal scheduling. Moreover, the number of conflicts is reduced by 100% after implementing the scheduling model. Research outcomes exhibit the potential to obtain practical arrival schedules with limiting controller workload. Implications of this work hint towards better stability and performance of the TMA and the overall Air Traffic Management (ATM) system.

Fredholm separating maps on continuous function spaces

For locally compact Hausdorff spaces X and Y, we initiate a study of Fredholm separating maps T from C(X) into C(Y). Our key aim is to completely recognize the structures of the kernel space and the range space of T in terms of merging points to show that X and Y are homeomorphic after removing related finite subsets; as the main result, we describe the interaction between continuity of T and closedness of its range; in particular, under certain condition, we conclude that T can be represented as a weighted composition operator.