arXiv subjects

Mengran Li

Publications and source records attributed to Mengran Li.

24 records · Page 2Linked to original sources

arXiv2024

TAS-TsC: A Data-Driven Framework for Estimating Time of Arrival Using Temporal-Attribute-Spatial Tri-space Coordination of Truck Trajectories

Accurately estimating time of arrival (ETA) for trucks is crucial for optimizing transportation efficiency in logistics. GPS trajectory data offers valuable information for ETA, but challenges arise due to temporal sparsity, variable sequence lengths, and the interdependencies among multiple trucks. To address these issues, we propose the Temporal-Attribute-Spatial Tri-space Coordination (TAS-TsC) framework, which leverages three feature spaces-temporal, attribute, and spatial-to enhance ETA. Our framework consists of a Temporal Learning Module (TLM) using state space models to capture temporal dependencies, an Attribute Extraction Module (AEM) that transforms sequential features into structured attribute embeddings, and a Spatial Fusion Module (SFM) that models the interactions among multiple trajectories using graph representation learning.These modules collaboratively learn trajectory embeddings, which are then used by a Downstream Prediction Module (DPM) to estimate arrival times. We validate TAS-TsC on real truck trajectory datasets collected from Shenzhen, China, demonstrating its superior performance compared to existing methods.

cs.AI↗

arXiv2024

Effective Finite Time Stability Control for Human-Machine Shared Vehicle Following System

With the development of intelligent connected vehicle technology, human-machine shared control has gained popularity in vehicle following due to its effectiveness in driver assistance. However, traditional vehicle following systems struggle to maintain stability when driver reaction time fluctuates, as these variations require different levels of system intervention. To address this issue, the proposed human-machine shared vehicle following assistance system (HM-VFAS) integrates driver outputs under various states with the assistance system. The system employs an intelligent driver model that accounts for reaction time delays, simulating time-varying driver outputs. A control authority allocation strategy is designed to dynamically adjust the level of intervention based on real-time driver state assessment. To handle instability from driver authority switching, the proposed solution includes a two-layer adaptive finite time sliding mode controller (A-FTSMC). The first layer is an integral sliding mode adaptive controller that ensures robustness by compensating for uncertainties in the driver output. The second layer is a fast non-singular terminal sliding mode controller designed to accelerate convergence for rapid stabilization. Using real driver videos as inputs, the performance of the HM-VFAS was evaluated. Results show that the proposed control strategy maintains a safe distance under time-varying driver states, with the actual acceleration error relative to the target acceleration maintained within 0.5m/s~2 and the maximum acceleration error reduced by 1.2m/s~2. Compared to traditional controllers, the A-FTSMC controller offers faster convergence and less vibration, reducing the stabilization time by 27.3%.

eess.SY↗

arXiv2023

AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning

Attribute graphs are ubiquitous in multimedia applications, and graph representation learning (GRL) has been successful in analyzing attribute graph data. However, incomplete graph data and missing node attributes can have a negative impact on media knowledge discovery. Existing methods for handling attribute missing graph have limited assumptions or fail to capture complex attribute-graph dependencies. To address these challenges, we propose Attribute missing Graph Contrastive Learning (AmGCL), a framework for handling missing node attributes in attribute graph data. AmGCL leverages Dirichlet energy minimization-based feature precoding to encode in missing attributes and a self-supervised Graph Augmentation Contrastive Learning Structure (GACLS) to learn latent variables from the encoded-in data. Specifically, AmGCL utilizies feature reconstruction based on structure-attribute energy minimization while maximizes the lower bound of evidence for latent representation mutual information. Our experimental results on multiple real-world datasets demonstrate that AmGCL outperforms state-of-the-art methods in both feature imputation and node classification tasks, indicating the effectiveness of our proposed method in real-world attribute graph analysis tasks.

cs.LG↗

arXiv2022

Design and testing of LGAD sensor with shallow carbon implantation

The low gain avalanche detectors (LGADs) are thin sensors with fast charge collection which in combination with internal gain deliver an outstanding time resolution of about 30 ps. High collision rates and consequent large particle rates crossing the detectors at the upgraded Large Hadron Collider (LHC) in 2028 will lead to radiation damage and deteriorated performance of the LGADs. The main consequence of radiation damage is loss of gain layer doping (acceptor removal) which requires an increase of bias voltage to compensate for the loss of charge collection efficiency and consequently time resolution. The Institute of High Energy Physics (IHEP), Chinese Academy of Sciences (CAS) has developed a process based on the Institute of Microelectronics (IME), CAS capability to enrich the gain layer with carbon to reduce the acceptor removal effect by radiation. After 1 MeV neutron equivalent fluence of 2.5$\times$10$^{15}$ n$_{eq}$/cm$^{2}$, which is the maximum fluence to which sensors will be exposed at ATLAS High Granularity Timing Detector (HGTD), the IHEP-IME second version (IHEP-IMEv2) 50 $μ$m LGAD sensors already deliver adequate charge collection > 4 fC and time resolution < 50 ps at voltages < 400 V. The operation voltages of these 50 $μ$m devices are well below those at which single event burnout may occur.

physics.ins-det↗

arXiv2022

Low Gain Avalanche Detectors with Good Time Resolution Developed by IHEP and IME for ATLAS HGTD project

This paper shows the simulation and test results of 50um thick Low Gain Avalanche Detectors (LGAD) sensors designed by the Institute of High Energy Physics (IHEP) and fabricated by the Institute of Microelectronics of the Chinese Academy of Sciences (IME). Three wafers have been produced with four different gain layer implant doses each. Different production processes, including variation in the n++ layer implant energy and carbon co-implantation were used. Test results show that the IHEP-IME sensors with the higher dose of gain layer have lower breakdown voltages and higher gain layer voltages from capacitance-voltage properties, which are consistent with the TCAD simulation. Beta test results show that the time resolution of IHEP-IME sensors is better than 35ps when operated at high voltage and the collected charges of IHEP-IME sensors are larger than 15fC before irradiation, which fulfill the required specifications of sensors before irradiations for the ATLAS HGTD project.

physics.ins-det↗

arXiv2011

Cyclic and Inductive Calculi are equivalent

Brotherston and Simpson [citation] have formalized and investigated cyclic reasoning, reaching the important conclusion that it is at least as powerful as inductive reasoning (specifically, they showed that each inductive proof can be translated into a cyclic proof). We add to their investigation by proving the converse of this result, namely that each inductive proof can be translated into an inductive one. This, in effect, establishes the equivalence between first order cyclic and inductive calculi.

cs.LO↗