arXiv subjects

Xiang Dong

Publications and source records attributed to Xiang Dong.

17 recordsLinked to original sources

arXiv2025

Preemptive Spatiotemporal Trajectory Adjustment for Heterogeneous Vehicles in Highway Merging Zones

Aiming at the problem of driver's perception lag and low utilization efficiency of space-time resources in expressway ramp confluence area, based on the preemptive spatiotemporal trajectory Adjustment system, from the perspective of coordinating spatiotemporal resources, the reasonable value of safe space-time distance in trajectory pre-preparation is quantitatively analyzed. The minimum safety gap required for ramp vehicles to merge into the mainline is analyzed by introducing double positioning error and spatiotemporal trajectory tracking error. A merging control strategy for autonomous driving heterogeneous vehicles is proposed, which integrates vehicle type, driving intention, and safety spatiotemporal distance. The specific confluence strategies of ramp target vehicles and mainline cooperative vehicles under different vehicle types are systematically expounded. A variety of traffic flow and speed scenarios are used for full combination simulation. By comparing the time-position-speed diagram, the vehicle operation characteristics and the dynamic difference of confluence are qualitatively analyzed, and the average speed and average delay are used as the evaluation indices to quantitatively evaluate the performance advantages of the preemptive cooperative confluence control strategy. The results show that the maximum average delay improvement rates of mainline and ramp vehicles are 90.24 % and 74.24 %, respectively. The proposed strategy can effectively avoid potential vehicle conflicts and emergency braking behaviors, improve driving safety in the confluence area, and show significant advantages in driving stability and overall traffic efficiency optimization.

eess.SY↗

arXiv2025

Spatiotemporal Trajectory Tracking Method for Vehicles Incorporating Lead-Lag Judgement

In the domain of intelligent transportation systems, especially within the context of autonomous vehicle control, the preemptive holistic collaborative system has been presented as a promising solution to bring a remarkable enhancement in traffic efficiency and a substantial reduction in the accident rate, demonstrating a great potential of development. In order to ensure this system operates as intended, accurate tracking of the spatiotemporal trajectory is of crucial significance. Moreover, minimizing the tracking error is a necessary step in this process. To this end, a novel lead-lag judgment mechanism is proposed. This mechanism precisely quantifies the longitudinal positional deviation between the vehicle and the target trajectory over time, then the deviation is corrected with a real - time acceleration compensation strategy, as a result, the accuracy and reliability of trajectory tracking are significantly enhanced. Real - vehicle experiments were conducted in a dedicated test field to validate the feasibility of this innovative approach empirically. Subsequently, the obtained tracking data was subsequent processed using the lead-lag judgment mechanism. In this step, we carefully analyzed the spatiotemporal error patterns between the vehicle and the target trajectory under different alignments and speeds. Finally, using real highway speed and alignment data, we conducted comprehensive spatiotemporal trajectory tracking simulations. Through experiments and simulations, tracking errors maintained in an acceptable range and reasonable spatiotemporal distance is given during the preemptive merging process on highway ramps. Overall, this study offers valuable insights for highway ramp emerging safety. Future work can expand on these findings.

eess.SY↗

arXiv2024

Preemptive Holistic Collaborative System and Its Application in Road Transportation

Numerous real-world systems, including manufacturing processes, supply chains, and robotic systems, involve multiple independent entities with diverse objectives. The potential for conflicts arises from the inability of these entities to accurately predict and anticipate each other's actions. To address this challenge, we propose the Preemptive Holistic Collaborative System (PHCS) framework. By enabling information sharing and collaborative planning among independent entities, the PHCS facilitates the preemptive resolution of potential conflicts. We apply the PHCS framework to the specific context of road transportation, resulting in the Preemptive Holistic Collaborative Road Transportation System (PHCRTS). This system leverages shared driving intentions and pre-planned trajectories to optimize traffic flow and enhance safety. Simulation experiments in a two-lane merging scenario demonstrate the effectiveness of PHCRTS, reducing vehicle time delays by 90%, increasing traffic capacity by 300%, and eliminating accidents. The PHCS framework offers a promising approach to optimize the performance and safety of complex systems with multiple independent entities.

eess.SY↗

arXiv2024

DeepMuon: Accelerating Cosmic-Ray Muon Simulation Based on Optimal Transport

Cosmic muon imaging technology is increasingly being applied in various fields. However, simulating cosmic muons typically requires the rapid generation of a large number of muons and tracking their complex trajectories through intricate structures. This process is highly computationally demanding and consumes significant CPU time. To address these challenges, we introduce DeepMuon, an innovative deep learning model designed to efficiently and accurately generate cosmic muon distributions. In our approach, we employ the inverse Box-Cox transformation to reduce the kurtosis of the muon energy distribution, making it more statistically manageable for the model to learn. Additionally, we utilize the Sliced Wasserstein Distance (SWD) as a loss function to ensure precise simulation of the high-dimensional distributions of cosmic muons. We also demonstrate that DeepMuon can accurately learn muon distribution patterns from a limited set of data, enabling it to simulate real-world cosmic muon distributions as captured by detectors. Compared to traditional tools like CRY, DeepMuon significantly increases the speed of muon generation at sea level. Furthermore, we have developed a pipeline using DeepMuon that directly simulates muon distributions in underwater environments, dramatically accelerating simulations for underwater muon radiography and tomography. For more details on our open-source project, please visit https://github.com/wangab0/deepmuon.

hep-ex↗

arXiv2024

Enhancing Expressway Ramp Merge Safety and Efficiency via Spatiotemporal Cooperative Control

In the context of autonomous driving on expressways, the issue of ensuring safe and efficient ramp merging remains a significant challenge. Existing systems often struggle to accurately assess the status and intentions of other vehicles, leading to a persistent occurrence of accidents despite efforts to maintain safe distances. This study proposes a novel spatiotemporal cooperative control approach integrating vehicle-road coordination to address this critical issue. A comprehensive methodology is developed, beginning with the calculation of safe distances under varying spatiotemporal conditions. This involves considering multiple factors, including vehicle speed differentials, positioning errors, and clock synchronization errors. Subsequently, an advanced vehicle conflict risk evaluation model is constructed. By incorporating collision acceleration and emergency acceleration as key parameters, this model offers a more accurate and detailed evaluation of potential risks during the ramp merging process. Based on the calculated safe distances and conflict risk evaluations, a mainline priority coordinated control method is formulated. This method enables the pre-planning of vehicle trajectories, effectively reducing conflicts among vehicles. Through rigorous simulations using diverse traffic volume and speed scenarios, the efficacy of the proposed strategy is validated. The results demonstrate remarkable improvements, with the average delay time reduced by an impressive 97.96% and fuel consumption decreased by 6.01%. These outcomes indicate that the proposed approach not only enhances the speed of vehicle merging but also significantly reduces latency and fuel consumption, thereby enhancing the overall performance of ramp merging operations.

eess.SY↗

arXiv2024

Design and Testing for Steel Support Axial Force Servo System

Foundation excavations are deepening, expanding, and approaching structures. Steel supports measure and manage axial force. The study regulates steel support structure power during deep excavation using a novel axial force management system for safety, efficiency, and structural integrity. Closed-loop control changes actuator output to maintain axial force based on force. In deep excavation, the servo system regulates unstable soil, side pressure, and structural demands. Modern engineering and tech are used. Temperature changes automatically adjust the jack to maintain axial force. Includes hydraulic jacks, triple-acting cylinders, temperature, and deformation sensors, and automatic control. Foundation pit excavation is dynamic, yet structure tension is constant. There is no scientific way to regulate axial force foundation pit excavation. The revolutionary Servo system adjusts temperature, compression, and axial force to deform pits. System control requires foundation pit direction detection and modification. This engineering method has performed effectively for deep foundation pit excavation at railway crossings and other infrastructure projects. The surrounding protective structure may reduce the steel support's axial stress, making deep foundation excavation safe and efficient. Keywords: Servo systems, Steel strut support design, Deformation control, Monitoring and control, Deep excavation projects.

eess.SY↗

arXiv2024

Spatio-temporal cooperative control Method of Highway Ramp Merge Based on Vehicle-road Coordination

The merging area of highway ramps faces multiple challenges, including traffic congestion, collision risks, speed mismatches, driver behavior uncertainties, limited visibility, and bottleneck effects. However, autonomous vehicles engaging in depth coordination between vehicle and road in merging zones, by pre-planning and uploading travel trajectories, can significantly enhance the safety and efficiency of merging zones.In this paper,we mainly introduce mainline priority cooperation method to achieve the time and space cooperative control of highway merge.Vehicle-mounted intelligent units share real-time vehicle status and driving intentions with Road Section Management Units, which pre-plan the spatiotemporal trajectories of vehicle travel. After receiving these trajectories, Vehicle Intelligent Units strictly adhere to them. Through this deep collaboration between vehicles and roads, conflicts in time and space during vehicle travel are eliminated in advance.

eess.SY↗

arXiv2024

Holistic view of the road transportation system based on real-time data sharing mechanism

Traditional manual driving and single-vehicle-based intelligent driving have limitations in real-time and accurate acquisition of the current driving status and intentions of surrounding vehicles, leading to vehicles typically maintaining appropriate safe distances from each other. Yet, accidents still frequently occur, especially in merging areas; meanwhile, it is difficult to comprehensively obtain the conditions of road infrastructure. These limitations not only restrict the further improvement of road capacity but also result in irreparable losses of life and property. To overcome this bottleneck, this paper constructs a space-time global view of the road traffic system based on a real-time sharing mechanism, enabling both road users and managers to timely access the driving intentions of nearby vehicles and the real-time status of road infrastructure.

cs.CY↗

arXiv2024

SwdFold:A Reweighting and Unfolding method based on Optimal Transport Theory

High-energy physics experiments rely heavily on precise measurements of energy and momentum, yet face significant challenges due to detector limitations, calibration errors, and the intrinsic nature of particle interactions. Traditional unfolding techniques have been employed to correct for these distortions, yet they often suffer from model dependency and stability issues. We present a novel method, SwdFold, which utilizes the principles of optimal transport to provide a robust, model-independent framework to estimate the probability density ratio for data unfolding. It not only unfold the toy experimental event by reweighted simulated data distributions closely with true distributions but also maintains the integrity of physical features across various observables. We can expect it can enable more reliable predictions and comprehensive analyses as a high precision reweighting and unfolding tool in high-energy physics.

physics.data-an↗

arXiv2024

PWACG: Partial Wave Analysis Code Generator supporting Newton-conjugate gradient method

This paper introduces a novel Partial Wave Analysis Code Generator (PWACG) that automatically generates high-performance partial wave analysis codes. This is achieved by leveraging the JAX automatic differentiation library and the jinja2 template engine. The resulting code is constructed using the high-performance API of JAX, and includes support for the Newton's Conjugate Gradient optimization method, as well as the full utilization of parallel computing capabilities offered by GPUs. By harnessing these advanced computing techniques, PWACG demonstrates a significant advantage in efficiently identifying global optimal points compared to conventional partial wave analysis software packages.

physics.comp-ph↗

arXiv2023

Event generation and consistency tests with sliced Wasserstein distance in high-energy physics

In the field of modern high-energy physics research, there is a growing emphasis on utilizing deep learning techniques to optimize event simulation, thereby expanding the statistical sample size for more accurate physical analysis. Traditional simulation methods often encounter challenges when dealing with complex physical processes and high-dimensional data distributions, resulting in slow performance. To overcome these limitations, we propose a solution based on deep learning with the sliced Wasserstein distance as the loss function. By employing an advanced transformer learning architecture, we initiate the learning process from a Monte Carlo sample and generate high-dimensional data. Through the integration of the sliced Wasserstein distance with the permutation test, we introduce a novel, statistically rigorous, and more sensitive metric for assessing the distribution differences, which significantly outperforms other metrics in detecting subtle distributional shifts, further validating its effectiveness for precise evaluation in high-energy physics generative models and high-dimensional consistency test. The generated data samples maintain all the original distribution features from a limited number of training samples, as evidenced by their successful passage of all common consistency tests using a test sample size of the same order of statistical magnitude. This development opens up new possibilities for improving event simulation and high-dimensional consistency tests in high-energy physics research.

physics.comp-ph↗

arXiv2023

Application of Deep Learning Methods Combined with Physical Background in Wide Field of View Imaging Atmospheric Cherenkov Telescopes

The HADAR experiment, which will be constructed in Tibet, China, combines the wide-angle advantages of traditional EAS array detectors with the high sensitivity advantages of focused Cherenkov detectors. Its physics objective is to observe transient sources such as gamma-ray bursts and counterparts of gravitational waves. The aim of this study is to utilize the latest AI technology to enhance the sensitivity of the HADAR experiment. We have built training datasets and models with distinctive creativity by incorporating relevant physical theories for various applications. They are able to determine the kind, energy, and direction of incident particles after careful design. We have obtained a background identification accuracy of 98.6%, a relative energy reconstruction error of 10.0%, and an angular resolution of 0.22-degrees in a test dataset at 10 TeV. These findings demonstrate the enormous potential for enhancing the precision and dependability of detector data analysis in astrophysical research. Thanks to deep learning techniques, the HADAR experiment's observational sensitivity to the Crab Nebula has surpassed that of MAGIC and H.E.S.S. at energies below 0.5 TeV and remains competitive with conventional narrow-field Cherenkov telescopes at higher energies. Additionally, our experiment offers a fresh approach to dealing with strongly connected scattered data.

astro-ph.IM↗

arXiv2023

Fraction Constraint in Partial Wave Analysis

To resolve the non-convex optimization problem in partial wave analysis, this paper introduces a novel approach that incorporates fraction constraints into the likelihood function. This method offers significant improvements in both the efficiency of pole searching and the reliability of resonance selection within partial wave analysis.

hep-ex↗

arXiv2023

Giant Hall Switching by Surface-State-Mediated Spin-Orbit Torque in a Hard Ferromagnetic Topological Insulator

Topological insulators (TI) and magnetic topological insulators (MTI) can apply highly efficient spin-orbit torque (SOT) and manipulate the magnetization with their unique topological surface states with ultra-high efficiency. Here, we demonstrate efficient SOT switching of a hard MTI, V-doped (Bi,Sb)2Te3 (VBST) with a large coercive field that can prevent the influence of an external magnetic field. A giant switched anomalous Hall resistance of 9.2 $k\Omega$ is realized, among the largest of all SOT systems, which makes the Hall channel a good readout and eliminates the need to fabricate complicated magnetic tunnel junction (MTJ) structures. The SOT switching current density can be reduced to $2.8\times10^5 A/cm^2$. Moreover, as the Fermi level is moved away from the Dirac point by both gate and composition tuning, VBST exhibits a transition from edge-state-mediated to surface-state-mediated transport, thus enhancing the SOT effective field to $1.56\pm 0.12 T/ (10^6 A/cm^2)$ and the interfacial charge-to-spin conversion efficiency to $3.9\pm 0.3 nm^{-1}$. The findings establish VBST as an extraordinary candidate for energy-efficient magnetic memory devices.

cond-mat.mes-hall↗

arXiv2023

High speed free-space optical communication using standard fiber communication component without optical amplification

Free-space optical communication (FSO) can achieve fast, secure and license-free communication without need for physical cables, making it a cost-effective, energy-efficient and flexible solution when the fiber connection is unavailable. To establish FSO connection on-demand, it is essential to build portable FSO devices with compact structure and light weight. Here, we develop a miniaturized FSO system and realize 9.16 Gbps FSO between two nodes that is 1 km apart, using a commercial single-mode-fiber-coupled optical transceiver module without optical amplification. Using our 4-stage acquisition, pointing and tracking (APT) systems, the tracking error is within 3 μrad and results an average link loss of 13.7 dB, which is the key for this high-bandwidth FSO demonstration without optical amplification. Our FSO link has been tested up to 4 km, with link loss of 18 dB that is limited by the foggy weather during the test. Longer FSO distances can be expected with better weather condition and optical amplification. With single FSO device weight of only 9.5 kg, this result arouses massive applications of field-deployable high-speed wireless communication.

eess.SP↗

arXiv2022

Tensor amplitudes for partial wave analysis of $ψ\toΔ\barΔ$ within helicity frame

We have derived the tensor amplitudes for partial wave analysis of $ψ\toΔ\barΔ$, $Δ\to p π$ within the helicity frame, as well as the amplitudes for the other decay sequences with same final states. These formulae are practical for the experiments measuring $ψ$ decaying into $p \bar{p}π^+ π^-$ final states, such as BESIII with its recently collected huge $J/ψ$ and $ψ(2S)$ data samples.

hep-th↗

arXiv2021

Giant nonlinear anomalous Hall effect induced by spin-dependent band structure evolution

Anomalous Hall effect (AHE) is the key transport signature unlocking topological properties of magnetic materials. While AHE is usually proportional to the magnetization, the nonlinearity suggests the existence of complex magnetic and electron orders. Nonlinear AHE includes the topological Hall effect (THE) that has been widely used to identify the presence of spin chirality in real space. But it can in principle be induced by band structure evolution via Berry curvatures in the reciprocal space. This effect has been largely overlooked due to the intertwined mechanisms in both real and reciprocal spaces. Here, we observed a giant nonlinear AHE with the resistivity up to 383.5 uohm cm, contributing unprecedentedly 97% of the total Hall response in EuCd2As2. Moreover, it can be further enhanced by tilting the magnetic field 30° away from [001] direction, reaching a large anomalous Hall angle up to 21%. Although it shows exactly the same double-peak feature as THE, our scaling analysis and first-principles calculations reveal that the Berry phase is extremely sensitive to the spin canting, and nonlinear AHE is a consequence of band structure evolution under the external magnetic fields. When the spins gradually tilt from the in-plane antiferromagnetic ground state to out-of-plane direction, band crossing and band inversion occur, introducing a bandgap at Γ point at a canting angle of 45°. That contributes to the enhancement of Berry curvature and consequently a large intrinsic Hall conductivity. Our results unequivocally reveal the sensitive dependence of band structures on spin tilting process under external magnetic fields and its pronounced influence on the transport properties, which also need to be taken into consideration in other magnetic materials.

cond-mat.mtrl-sci↗