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A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance

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Journal of Bionic engineering 2025年第1期22卷 383-397页

作者： Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu610031China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031China

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization *** integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization *** effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective ***,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy *** research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关键词： Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation

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Toward Throughput Optimization: A Congestion Control Scheme for CAM Delivery via NR Sidelink

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IEEE Internet of Things Journal 2025年第9期12卷 12206-12218页

作者： Fu, Qiang Liu, Jiajia Wang, Jiadai Kong, Jun Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology The School of Cybersecurity Shaanxi Xi’an710072 China

In the new radio (NR) vehicle-to-everything (V2X),congestion control (CC) is adopted to reduce channel occupancy and improve delivery performance. However, most existing CC schemes decide to adjust the number of messages while ignoring the throughput requirements, which may lead to serious traffic accidents due to the loss of information. In light of this, we investigate the performance of cooperative awareness messages (CAMs) delivery via NR sidelink under different CC configurations, including the modulation and coding scheme, the semi-persistent scheduling-based resource allocation, as well as the CAM transmission period. Specifically, taking into account these configurations, we develop a theoretical framework to capture two metrics of CAM delivery, namely the effective throughput and the channel busy ratio, based on the stochastic geometry approach. With the help of this framework, we propose the CC-(m,n,τ ) scheme, which is able to optimize the two metrics simultaneously by properly tuning the parameters m, n, and τ . Extensive numerical results show that the proposed scheme is highly beneficial for efficient and reliable CAM delivery via NR sidelink, particularly in scenarios with high vehicle densities. © 2014 IEEE.

关键词： Congestion control (communication)

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Multi-Label MambaOut for Quality Assessment of Low-Field Pediatric Brain MR Images 1st

Multi-Label MambaOut for Quality Assessment of Low-Field P...

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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2024

作者： Zhu, Yueyue Jiang, Haotian Cai, Rongqing Chen, Geng National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031830105

Magnetic Resonance Imaging (MRI) can be utilized to study the structure of pediatric brains non-invasively. In practice, low-field MRI scanners are widely adopted for pediatric brain imaging. However, the corresponding acquired MRI data usually suffers from severe artifacts, such as noise and motion. Therefore, an effective Quality Assessment (QA) method is essential. To this end, we design a Multi-Label MambaOut (MLMambaOut) model for the low-field pediatric brain MRI QA challenge. Specifically, we view this challenge as a multi-label classification task, utilizing four stages of gated convolution neural network blocks and ML-Decoder to finish the classification with class balance loss. Furthermore, we explore the performance of Mamba and some advanced models for this challenge. We performed extensive experiments on the challenge data, which is low-field and corrupted with seven kinds of artifacts. The results show that our MLMambaOut achieves superior classification results compared with other methods. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Joint Task Partitioning and Resource Allocation in UAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 15453-15466页

作者： Liang, Hongbin Zhang, Han Ale, Laha Hong, Xintao Wang, Lei Jia, Qiong Zhao, Dongmei Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Chinese Academy of Sciences Laboratory of Noise and Vibration Research Institute of Acoustics Beijing100190 China McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs' restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods. © 2014 IEEE.

关键词： Convex optimization

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Fundamental Diagram Modeling of Mixed Traffic Flow Considering Dedicated Lanes for Human-Driven Vehicles

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Yi Xu, Zeqi Wu, Yunxia Jiang, Yangsheng Yao, Zhihong Southwest Jiaotong University School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Sichuan610031 China

To mitigate the impact of human-driven vehicles (HDVs) on connected and automated vehicles (CAVs) in mixed traffic environments, the implementation of dedicated lanes has been proposed to achieve partial separation between CAVs and HDVs, thereby improving the operational efficiency of both CAVs and the road segment. The lane management policy, where dedicated lanes for HDV (HDLs) and general lanes (GLs) coexist on a road segment, is referred to as the (G, H) policy. This paper proposes a multi-lane fundamental diagram model for mixed traffic flow and aims to investigate the effects of HDL configuration on the efficiency of road segments under the (G, H) policy. Firstly, different car-following modes in mixed traffic flow are analyzed, and various car-following models are employed to characterize the mixed traffic flow. Secondly, two lane selection principles are introduced to describe the lane choice behavior of HDVs under the (G, H) policy. Based on these principles, five equilibrium states that may exist on the road segment under the (G, H) policy are analyzed. Subsequently, a multi-lane fundamental diagram model incorporating HDL is derived based on the lane selection principles of HDV. Finally, numerical analysis is conducted to investigate the influence of lane configuration schemes under the (G, H) policy on the distribution of equilibrium states, fundamental diagram, and capacity. The results indicate that: (1) Based on the lane choice behavior of vehicles, the equilibrium states of road segment can be classified into five types. The distribution of each equilibrium state under different traffic conditions only depends on the proportion of HDL to the total number of lanes on road segment. A higher number of HDL leads to a reduced applicability of the (G, H) policy under different traffic conditions. (2) Under different penetration rates, as density increases, the overall traffic volume of road segment initially increases and then decreases until reaching the

关键词： Highway administration

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Analysis of Line Slope Between Urban Rial Stations Based on Energy-Saving Operation 11th

Analysis of Line Slope Between Urban Rial Stations Based on ...

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11th International Conference on Traffic and transportation Studies, ICTTS 2024

作者： Zhang, Zengyong Han, Jianlong Xu, Qi Economic Research Department CRRC Academy Co. Ltd. Beijing China Key Laboratory of Big Data Application Technology for Integrated Transportation in Transportation Industry Beijing Jiaotong University Beijing China

ISBN: (纸本)9789819796397

Green transportation is the object of transportation in the future, and reducing energy consumed in transportation is of great significance to sustainable development of the society. This paper mainly studies the impact of line slope between stations on the energy consumed by the train. The paper starts with description of typical line conditions between stations. Three control modes: traction, coasting and braking are considered in the process of train running. Based on these, the basic dynamic model of the train running energy consumption, and the corresponding energy-saving operation algorithm are set. Then, in the presumption that the distance between stations of urban rail transit is 3 km, energy consumption curve of the train and the corresponding velocity curve distribution of different slope and slope length are analyzed, and the optimal combination between station section line slope and train energy-saving operation was obtained. This provides a reference for the design of station section lines based on train energy-saving operation in urban rail transit. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Energy saving

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Attaining the stabilizing solution of model unavailable modified algebraic Riccati equation using Q-learning algorithm

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Information Sciences 2025年 717卷

作者： Gao, Jie Feng, Tao Yan, Fei School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

This paper attempts first to solve the discrete-time modified algebraic Riccati equation (MARE) when the system model is completely unavailable. To achieve this, a new iterative algorithm is proposed to solve the MARE based on its equivalent discrete-time algebraic Riccati equation (DARE) which has an identical solution. The algorithm iterates between two steps: 1) the solution improvement of the equivalent DARE;2) the input weighting improvement. It is proved theoretically that the algorithm strictly converges to the stabilizing solution of the MARE. As an outstanding advantage over the existing methods, the initiation of the algorithm becomes fairly simple which can be initialized by an arbitrary positive input weighting for the single-input scenario or a pre-given input weighting matrix of a sufficiently large magnitude for the multi-input scenario. Furthermore, the proposed algorithm provides an iterative structure which is particularly suitable for developing the Q-learning (QL) algorithm where only the input/output data of the related linear quadratic regulator (LQR) problem is required, therefore the MARE can be solved successfully without using the system model. Finally, numerical simulation examples are given to verify the effectiveness of the theoretical results and the developed algorithm. © 2025 Elsevier Inc.

关键词： Riccati equations

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LightST: A Simplifying Spatio-Temporal Graph Neural Network for Traffic Flow Forecasting

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IEEE Transactions on big data 2025年

作者： Hu, Jie Zheng, Taichuan Peng, Lilan Teng, Fei Du, Shengdong Li, Tianrui Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology China

Traffic flow forecasting task plays an essential role in intelligent transportation systems. Accurately capturing the intricate spatio-temporal dependencies in traffic network signals is the core of precise prediction. Recently, a paradigm that models spatio-temporal dependencies through graph neural networks and time series models has become one of the most promising methods to solve this problem. However, existing methods still have limitations due to ineffectively modeling dynamic spatial dependencies and high time and space complexity. To address these issues, we propose a simplifying and powerful general spatio-temporal traffic flow forecasting model called LightST. Specifically, LightST first embeds temporal covariates and spatial position information to enhance the spatio-temporal modeling capabilities. Then, stacked temporal linear layers are introduced to capture temporal dependencies efficiently. Finally,we propose a concise adaptive spatio-temporal embedding graph convolution method to extract implicit spatial dependencies over time via dynamic graph convolution with adaptive spatio-temporal embedding graph generation. Extensive experiment results on four public traffic flow datasets demonstrate the superiority of our LightST concerning computational efficiency and prediction performance. © 2025 IEEE. All rights reserved,

关键词： Graph embeddings

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Cooperative Eco-Driving for Mixed Platoons with Heterogeneous Energy at a Signalized Intersection Based on a Mixed Space-Time-State Network

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IEEE Transactions on Vehicular technology 2025年

作者： Xiao, Guosheng Wu, Yunxia Jiang, Yangsheng Ran, Bin Yao, Zhihong Southwest Jiaotong University School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Sichuan610031 China University of Wisconsin-Madison Department of Civil and Environmental Engineering MadisonWI53706 United States

With the ongoing trend of vehicle automation and electrification, the future will present a mixed traffic environment with different automation and energy consumption types. However, current research focuses more on fully connected or homogeneous energy consumption scenarios in eco-driving, ignoring the impact of human-driven vehicles and electric vehicles (EVs) with high energy efficiency and regenerative braking capabilities. This study proposes a cooperative eco-driving framework for mixed vehicle platoons with heterogeneous energy at a signalized intersection. A Mixed Space-Time-State Network is developed to optimize the trajectory of connected autonomous vehicles (CAVs), while predicting and describing the trajectories of human-driven vehicles. Then, a modified dynamic programming algorithm is formulated, which converts the highly nonlinear optimization problem into a state-space search problem. The solving efficiency is further improved by 85% with a feasible state space reduction algorithm, which can meet the real-time computational requirements. The result indicates that cooperation within the mixed platoon can smooth out the trajectory while considering the composition of energy consumption. In addition, the energy efficiency can be improved by 20.39% with a low CAV penetration rate. © 1967-2012 IEEE.

关键词： Dynamic programming

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Electrochemical dissolution, reduction, and nucleation mechanisms of molybdenum in NaCl-KCl molten salt systems

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Journal of Materials Science and technology 2025年 238卷 45-54页

作者： Lv, Hongzhan Zhang, Liwen Xi, Xiaoli Nie, Zuoren Collaborative Innovation Center of Capital Resource-Recycling Material Technology Beijing University of Technology Beijing100124 China Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

This study investigates the anodic dissolution and electrochemical behavior of molybdenum in a NaCl-KCl molten salt system at 1023 K. The anodic dissolution process was systematically analyzed, revealing a sequential oxidation pathway of molybdenum into high-valence ions (Mo6+, Mo5+, Mo4+) under varying electrolysis potentials. Electrochemical Impedance Spectroscopy (EIS) demonstrated that the dissolution is governed by both charge transfer and diffusion mechanisms, with reduced impedance at higher potentials facilitating molybdenum dissolution. The reduction behavior of dissolved molybdenum ions was further explored using cyclic voltammetry (CV) and square wave voltammetry (SWV), confirming a multi-step reduction process controlled by diffusion and high reversibility. Nucleation studies using chronoamperometry established that molybdenum deposition follows an instantaneous nucleation mechanism. Morphological analysis of cathodic deposits revealed that current density significantly influences particle size, transitioning from nano-sized spherical particles to larger equiaxed crystals with increasing current density. These findings provide a comprehensive understanding of molybdenum's electrochemical properties in molten salts, offering valuable insights for optimizing electrolysis processes and advancing molybdenum-based material production. © 2025

关键词： Cyclic voltammetry

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