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The free-piston engine represents a paradigm shift in internal combustion engine technology, with its unique structure promising efficiency gains. However, injection parameters are one of the core elements of free-piston engine performance. This study employs computational fluid dynamics analysis to optimize the spray cone angle and start of injection timing for a two-stroke dual-piston opposed free-piston engine equipped with a flat-head combustion chamber. A three-dimensional transient model incorporating dynamic adaptive mesh refinement was constructed by using CONVERGE 3.0 software. The results indicate that a spray cone angle of 25° achieves optimal fuel distribution, yielding a peak indicated thermal efficiency of 42.14% and an indicated mean effective pressure of 9.08 bar. Crucially, advancing the ignition timing to 215°CA improves mixture homogeneity but simultaneously increases peak cylinder temperatures and NOx. Conversely, delayed start of injection timings reduces NO
Xu, ZhaopingYang, ShenaoLiu, Liang
To better match lane usage with changing traffic needs at intersections, this study proposes a method that uses deep reinforcement learning to optimize variable guidance lanes. We apply the DDPG algorithm and introduce a feature weight adjustment mechanism that changes in real time. It reacts to key traffic indicators such as vehicle flow, average delay, and peak delay. This helps the model respond more flexibly and improves its ability to handle different situations. To make the output actions easier to manage, we revise the sigmoid function used for discretization. The reward function is also designed carefully, aiming to keep lane changes smooth and stable. We test our method in a SUMO-simulated intersection. The results show that it outperforms both fixed lane strategies and standard DDPG models. It reduces delays, lowers queue lengths, and moves more traffic through the intersection, proving its value in real-world-like settings.
Zhang, WeiZhang, Fusheng
As one of the main indexes of functional safety evaluation, controllability is of critical significance. According to ISO26262 standard, by analyzing the impact of potential faults such as unexpected torque and regenerative braking force loss on vehicle controllability under different working conditions, this paper designs a vehicle controllability test scheme under abnormal motor function under multiple scenarios such as straights, lane changes and curves, and builds a test scheme under abnormal motor function. The mapping relationship between vehicle dynamic state data and controllability level provides a new idea for quantitative analysis of vehicle controllability.
Yang, XuezhuHe, LeiLi, ChaoRen, Zhiqiang
Aiming at the problem of efficiency loss caused by the independent optimization of traditional vehicle - cargo matching and route planning, this paper proposes a spatio - temporal collaborative optimization model. By constructing three - dimensional decision variables to describe the “vehicle - cargo - route” mapping relationship, a multi - objective mixed - integer programming model considering transportation costs, time - window constraints, and carbon emissions is established. An improved NSGA - II algorithm is designed to solve the Pareto optimal solution set, and the TOPSIS method is combined to achieve scheme optimization. Experiments show that the collaborative optimization model reduces the comprehensive cost by an average of 12.7% and the vehicle empty - running rate by 18.4% compared with the traditional two - stage method.
Yang, MeiruLiu, Jian
This study investigates urban traffic congestion optimisation strategies based on V2X technology. V2X technology (Vehicles and Internet of Everything) aims to alleviate urban traffic congestion, improve access efficiency, and reduce tailpipe emissions through real-time collection and fusion of traffic data to optimise traffic signal control and path planning. The efficacy of the optimisation strategies under different V2X penetration rates is evaluated by conducting multi-factor orthogonal experiments in different typical congestion scenarios. The experimental results show that the V2X-based signal optimisation, path induction, and event response combination strategies exhibit significant optimisation effects in all three scenarios: node bottleneck, corridor congestion, and event induction. Under the condition of 100% penetration, the combined strategy reduces delay by 41.9% in the node bottleneck scenario, improves accessibility by 28.1% in the corridor congestion scenario, and
Xi, ChaohuLi, JiashengQu, FengzhenLiu, HongjunLiu, XiaoruiWang, Chunpeng
This study investigates the critical factors influencing the performance of hydro-pneumatic suspension systems (HPSS) in mining explosion-proof engineering vehicles operating in complex underground coal mine environments. To address challenges such as poor ride comfort and insufficient load-bearing capacity under harsh mining conditions, a two-stage pressure HPSS was analyzed through integrated numerical modeling and field validation. A mathematical model was established based on the structural principles of the suspension system, focusing on key parameters including cylinder bore (195–255 mm), piston area (170–210 mm), damping orifice diameter (7–8 mm), check valve flow area, and accumulator configurations (low-pressure: 1.2 MPa, high-pressure: 6 MPa). Experimental trials were conducted in active coal mines, simulating typical mining scenarios such as uneven road surfaces (120 mm obstacles), heavy-load gangue transportation, and confined-space operations in thin coal seams (<1.5 m
Song, YanLiang, Yufang
With more 5G base stations coming into play, making an accurate assessment of RF-EMF exposure currently faces increasing demand to check if they meet regulatory requirements and ensure people’s safety. We present here PSF-Net, a novel deep learning network by uniting TabPFN’s meta-learned prior knowledge and SAINT’s dual attention structure; its use makes it particularly suitable to deal with applications like prediction of downlink power density and radiation level classification under different conditions within various kinds of 5G cell. A major component in the design of this approach is an uncertainty-aware gating block that determines the optimal weighting for each model output—TabPFN or SAINT—based on the estimated prediction variance as quantified via Monte Carlo sampling during training or the prediction variance calculated using inference-time dropout. In addition, a residual multi-layer perceptron (MLP) is also included to extract refined fused features and maintain a steady
Zhang, YanjinYu, Zefeng
(TC)The paper presents a designed and evaluated optimal traction control (TC) strategy for unmanned agriculture vehicle, where onboard sensors acquire various real-time information about wheel speed, load sharing, and terrain characteristics to achieve the precise control of the powertrain by establishing an optimal control command; moreover, the developed AMT-adaptive SMC combines the AMT adaptive control algorithm and the SMC to implement the dynamic gear shifting, torque output, and driving mode switching to obtain an optimal power distribution according to different speed demand and harvest load. Based on the establishment of models of the autonomous agriculture vehicle and corresponding tire model, a MATLAB/Simulink method based on dynamic simulation is adopted to simulate the unmanned agricultural vehicle traversing different terrains conditions. The results from comparison show that the energy saving reaches 19.0%, rising from 2. 1 kWh/km to 1. 7 kWh/km, an increase in
Feng, ZhenghaoLu, YunfanGao, DuanAn, YiZhou, Chuanbo
The analysis of the current subsidy scheme for China Europe Express shows that its effectiveness is limited to lines starting from inland cities and lines with unsaturated demand. A bi-level subsidy optimization model was constructed and Tabu Search algorithm was applied to solve the optimization subsidy plan. The evaluation results of the optimization subsidy scheme indicate that it can more effectively increase the market share of CRE, regulate the balance of freight supply and demand to a certain extent, reduce capacity vacancies, and alleviate line congestion.
Mai, YuanyuanTian, Chunlin
With the rapid development of Internet of Vehicles (IoV) and cyber-physical systems (CPS), connected autonomous vehicles (CAVs) have also developed rapidly. However, at the same time, in-vehicle networks also face more security challenges, mainly in terms of resource constraints, dynamic attacks, protocol heterogeneity, and high real-time requirements. Firstly, the trade-offs between lightweight encryption primitives and their software and hardware collaborative design in terms of performance, resource overhead, and security strength are analyzed. Secondly, the resource efficiency of AI-based intrusion detection system (IDS) is evaluated at the edge. Finally, we propose a dynamic adaptive collaborative defense framework (DACDF), which integrates federated learning with dynamic weight distillation, blockchain authentication with lightweight verifiable delay function (Light-VDF) and cross-domain IDS with hierarchical attention feature fusion to deal with collaborative attacks in resource
Zhou, YouZhang, JiguiDing, KaniYang, Guozhi
To tackle persistent operational instability and excessive energy consumption in marine observation platforms under wave-induced disturbances, this paper introduces a novel ultra-low-power stabilization system based on pendulum dynamics. The system employs an innovative mechanical configuration to deliberately decouple the rotation axis from the center of mass, creating controlled dynamic asymmetry. In this behavior, the fixed axis serves as a virtual suspension pivot while the camera payload functions as a concentrated mass block. This configuration generates intrinsic gravitational restoring torque, enabling passive disturbance attenuation. And its passive foundation is synergistically integrated with an actively controlled brushless DC motor system. During platform oscillation, embedded algorithms detect angular motion reversals. In addition, their detection triggers an instantaneous transition from motor drive to regenerative braking mode, and transition facilitates bidirectional
Zhang, TianlinLiu, ShixuanXu, Yuzhe
Heavy-duty commercial vehicles (HDCVs) are the key mobile nodes in intelligent transportation systems (ITS). However, their complex operating conditions and the diversity of data sources (such as road conditions, driver behavior, traffic signals, and on-board sensors) present considerable difficulties for accurately estimating the state and perceiving the environment using a single modality of data. This requires effective multi-modal data fusion to enhance the control and decision-making capabilities of HDCVs. This paper addresses this need by proposing a customized multi-modal intelligent transportation data fusion framework for intelligent HDCVs. This paper presents a solution for establishing a multi-modal intelligent transportation data collection platform, including real-scene collection methods and simulation scene collection methods based on the SUMO-MATLAB joint simulation platform. Through three representative case studies, the application methods of multi-modal traffic data
Chen, ZhengxianWang, ShaoqiJiang, HuimingZhou, FojinWang, MingqiangLi, Jun
To address the limitations of conventional overspeed detection methods, this study proposes a vehicle overspeed detection approach based on the fusion of millimeter-wave radar (MWR) and vision sensors. The MWR captures target position and velocity data, while the vision sensor acquires vehicle image information. Radar-detected points are mapped onto visual images through coordinate transformation, and the Intersection over Union (IoU) method is employed to associate radar points with vision-detected vehicle bounding boxes. Subsequently, for radar-detected points exceeding the speed threshold, the corresponding vehicle images are identified, enabling real-time overspeed detection and data acquisition. This method not only facilitates prompt identification of speeding behavior but also extracts the associated vehicle images, ensuring both accuracy and informational integrity in overspeed monitoring. Experimental results demonstrate that the proposed method achieves high speed measurement
Li, YuanchenWu, ZhichaoXu, HaiboSong, LiangliangHuang, Hao
Accurate traffic flow prediction plays a crucial role in modern transportation management systems, enabling extensive applications ranging from congestion warning to optimized route planning. While current approaches have achieved progress in specific areas, they continue to face challenges such as multi-scale dynamics and constrained spatiotemporal modeling capacity. Addressing these limitations, we introduce a innovative model termed the Spatial-Temporal Fusion Convolution Transformer (STFCT). This framework integrates periodic patterns and traffic characteristics via adaptive spatiotemporal embeddings to produce a unified representation capturing both spatial and temporal relationships. Our architecture incorporates a gating mechanism for dynamic spatiotemporal integration, along with a temporal convolution component to simultaneously capture both short- and medium-term patterns. Experimental results from three different traffic datasets reveal STFCT’s advantages over competing
Zhou, JunhaoLiu, TingJiang, Yangwei
To enhance the predictive accuracy between seat structural parameters and crash performance, a hybrid model was constructed by coupling an Improved Particle Swarm Optimization (IPSO) algorithm with a Back Propagation Neural Network (BPNN). First, a finite element model for front and rear impact of automotive seats was established based on experimental data, and the model’s accuracy was verified. Subsequently, simulations were conducted, and the results were analyzed. The Energy Absorption Mass Ratio method was used to screen the design variables, ultimately selecting 10 thickness variables and 9 material variables as design variables. Latin Hypercube Sampling was employed to divide the dataset into a testing set and a training set. Then, the Particle Swarm Optimization (PSO) was enhanced with Levy flights and a local mutation strategy, utilizing the IPSO algorithm to optimize the initial weights and thresholds of the BPNN, resulting in the establishment of the IPSO-BPNN predictive
Qiu, YufeiLong, Jiangqi
Semi-trailer trains are the main force of highway freight. In a complex environment with multiple vehicles, accidents are easily caused by complex structures and driver operation problems. Intelligent technology is urgently needed to improve safety. In view of the shortcomings of existing research on its dedicated models and algorithms, this paper studies the intelligent decision-making and trajectory planning of semi-trailer trains under multiple vehicles. A local trajectory planning method based on global path planning and Frenet coordinate decoupling based on the improved A* algorithm is proposed. The smooth weight transition function and B-spline curve are introduced to optimize the global path. The polynomial function is combined with the acceleration rate to optimize the local trajectory. TruckSim, Prescan and Simulink are used to build a joint simulation platform for multi-condition verification. The simulation results show that the search efficiency of the improved A* algorithm
Song, ZeyuanGeng, Shuai
In contemporary society, where Global Navigation Satellite Systems (GNSS) are utilised extensively, their inherent fragility gives rise to potential hazards with respect to the safety of ship navigation. In order to address this issue, the present study focuses on an ASM signal delay measurement system based on software defined radio peripherals. The system comprises two distinct components: a transmitting end and a receiving end. At the transmitting end, a signal generator, a first time-frequency synchronisation device, and a VHF transmitting antenna are employed to transmit ASM signals comprising dual Barker 13 code training sequences. At the receiving end, signals are received via software-defined radio equipment, a second time-frequency synchronisation device, a computing host, and a VHF receiving antenna. Utilising sliding correlation algorithms enables accurate time delay estimation. The present study leverages the high performance and low cost advantages of the universal
Li, HaoSun, XiaowenWang, TianqiZhou, ZeliangWang, Xiaoye
Based on the TOD (Transit-Oriented Development) concept, this paper addresses the “last mile” issue in urban public transportation. It proposes a multidimensional decision-making model for identifying micro-circulation bus route areas. By integrating indicators such as the TOD comprehensive index, short-distance demand intensity, and branch network density, relevant data is processed using FME linking ArcGIS. The model combines entropy-weighted TOPSIS and unsupervised consensus clustering analysis techniques, utilizing ArcGIS spatial analysis functions to accurately identify priority deployment areas for micro-circulation buses. Taking Jiangbei District in Chongqing as an example, the model divides the study area into four types of traffic zones: (1) Core high-density areas, which require an increase in micro-circulation bus routes due to extremely high short-distance travel demand; (2) Periphery active population areas, which require flexible shuttle services due to transit gaps and
Jiang, TaoJia, XiaoyanLi, Jie
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Xie, DongxuanLi, DongyangZhang, YoukangZhao, YingjieHong, BaofengWang, Nan
The geological disasters along the Sichuan-Tibet Highway are frequent, and the traffic environment is complex. Traditional disaster reconnaissance methods struggle to meet the timeliness and accuracy requirements of emergency response. With the development of unmanned aerial vehicle (UAV) technology, it has significant advantages in rapid disaster information acquisition and complex terrain coverage. Considering the large elevation fluctuations, variable climate, and limited communication conditions in the study area, this paper focuses on UAV disaster reconnaissance in complex mountainous environments. By systematically summarizing and categorizing existing UAV disaster reconnaissance methods, this paper designs a UAV disaster reconnaissance system and applies it in practical engineering projects, providing technical support for disaster reconnaissance and emergency management along the Sichuan-Tibet Highway.
Wu, GuorongXu, HuayanChen, YunjinTang, LuweiMo, ShiyingLuo, ShuzhaoHuang, ZiyangLiu, Xianxin
To address the high security demands of HSR communication, this letter proposes a covert communication scheme using irregular intelligent transparent surfaces (ITSs) deployed on train windows. A joint optimization framework is developed to enhance covert rate under element constraints, involving ATS for topology design and NECE for beamforming and phase shift. Gradient descent is used to handle covert constraints. Simulations confirm that the proposed irregular ITS outperforms regular ITS in performance and robustness, offering a promising solution for future HSR covert communication.
Jia, JingwenGao, YunboXie, Jianli
In order to meet the demand for the transformation of traditional manufacturing industries into intelligent manufacturing, a virtual monitoring system for the production workshops of nuclear - key products has been built. There are problems such as poor environment, long distance and remote collaborative office in this production workshop, and managers lack information tools to master the workshop status in real time. In order to minimize the harm of nuclear radiation to the human body, in view of the problems of low transparency, poor real - time performance and low data integration in traditional two - dimensional forms, configuration software and video monitoring, a remote monitoring system for virtual workshops driven by digital models has been developed. This system realizes the remote dynamic display of real - time information in the workshop based on data collection and three - dimensional modeling technologies. Virtual monitoring technology improves the management efficiency of
Wu, YimingChen, RuiLi, Na
Aviation carbon verification plays a crucial role in China’s achievement of its “dual carbon goals”. Traditional manual sampling methods are difficult to meet the timeliness requirements of the rapidly increasing volume of flight data. A rapid verification system for flight carbon emissions designed based on process reengineering relies on three spatio-temporal verification methods: weekly cycle verification, flight segment verification, and flight tail number verification. A comprehensive verification framework that can replace manual sampling has been constructed. The system adopts a modular architecture, integrating the functions of data management and rapid verification. Experimental results show that in scenarios with 100,000 flight data, the average verification time of the system is 0.12 hours. Compared with manual methods, the efficiency has been greatly improved, and the f1 score has remained stable at over 89.5%. These findings confirm that the system has advantages in both
Ding, WeichenChen, Jingjie