A TD3-Based Reinforcement Learning Algorithm With Curriculum Learning for Adaptive Yaw Control in All-Wheel-Drive Electric Vehicles by Reza Jafari, Pouria Sarhadi, Amin Paykani, Shady S. Refaat, Pedram Asef

“…A novel artificial intelligence-based approach for the direct yaw control (DYC) of an all-wheel drive (AWD) electric vehicle (EV) is proposed in this paper. To improve adaptability and ability to handle nonlinearities via continuous learning, the proposed algorithm is built upon a twin delayed deep deterministic policy gradient (TD3) reinforcement learning (RL) algorithm for the optimal torque distribution across four wheels of the vehicle. …”
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ISSUE OF SIMULATOR CHOICE FOR MOBILE FACILITIES OF MILITARY VEHICLES MAINTENANCE AND ITS SOLUTION by I. Yu. Shevchenko, S. S. Poyarkov, S. S. Zinov’ev

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Detection of Road Rage in Vehicle Drivers Based on Speech Feature Fusion by Xiaofeng Feng, Chenhui Liu, Ying Chen

“…Six principal components are selected as detection features based on the proportion of the variance values of the principal components of each dimension. The sparrow search algorithm is used to optimise the support vector machine classifier, and an SSA-SVM road rage emotion detection model is established and trained to recognise road rage emotions in drivers. …”
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A Modularized Super Capactior Energy Storage System forMetro Train and Its Control Algorithm by CHEN Guangzan, LIU Lan, HUANG Deliang, YI Yunlan

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Design of AUV controller based on improved PPO algorithm by Desheng XU, Chunhui XU

“…ObjectiveIn order to improve the robustness of autonomous underwater vehicle (AUV) controllers to environment modeling errors, this paper proposes a reinforcement learning control strategy that introduces contextual information and a course-learning training mechanism. …”
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Research on Design of Control Method for Maglev Vehicles Based on V-shaped Guideway by MI Zhu, LI Yun-gang, CHENG Hu

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Dynamic Inspection System for Wheel Profile of High-speed Railway Vehicles by YANG Hui

“…Measuring wheel profile parameters is very important for the safety of running trains. Inspection of wheel profile parameters with laser light image was the mainstream technology. …”
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Research on Multi-Objective Optimization Design of High-Speed Train Wheel Profile Based on RPSTC-GJO by Mao Li, Hao Ding, Meiqi Wang, Xingda Yang, Bin Kong

“…Firstly, the chaotic map is introduced into the population initialization process of the golden jackal algorithm. In the later stage of the algorithm iteration, random disturbance is introduced with optimization algebra as the switching condition to obtain an improved optimization algorithm, and the performance index of the optimization algorithm is verified to be superior to other algorithms. …”
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Study of the Possibility to Combine Deep Learning Neural Networks for Recognition of Unmanned Aerial Vehicles in Optoelectronic Surveillance Channels by Vladislav Semenyuk, Ildar Kurmashev, Dmitriy Alyoshin, Liliya Kurmasheva, Vasiliy Serbin, Alessandro Cantelli-Forti

“…The authors implemented training, verification, and testing of the neural networks exploiting PyCharm 2024 IDE. …”
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Active Control of Shimmy in Articulated Single-Axle Straddle-Type Monorail Train by Jiachen Song, Liwei Zhang, Dongjin Zhu, Hui Liang

“…However, the issue of vehicle shimmy greatly restricts its promotion and application. …”
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Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm. by Xiaoying Yu, Hongsheng Su, Zeyuan Fan, Yu Dong

“…Therefore, this algorithm can be incorporated into the vehicle-mounted speed measurement module to automatically update the value of wheel diameter, thereby substantially reducing the manual work entailed therein and improving the effectiveness of measuring the speed and position of the train.…”
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SGD-TripleQNet: An Integrated Deep Reinforcement Learning Model for Vehicle Lane-Change Decision by Yang Liu, Tianxing Yang, Liwei Tian, Jianbiao Pei

“…To address these issues, this paper proposes a novel integrated deep reinforcement learning model called “SGD-TripleQNet” for autonomous vehicle lane-change decision-making. This method integrates three types of deep Q-learning networks (DQN, DDQN, and Dueling DDQN) and uses the Stochastic Gradient Descent (SGD) optimization algorithm to dynamically adjust the network weights. …”
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Combined electro-pneumatic braking matching control for smooth operation of the heavy-haul train by MA Zhiqiang, WANG Junyu, WEI Mi, WANG Qingyuan

“…In the current study, a multi-objective optimal operation model was established for heavy-haul trains firstly, considering the dynamic process in the application of air braking force, and the constraints due to the train operation environment and vehicle mechanical characteristics, and then was solved by the quadratic programming algorithm. …”
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A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders by Jorge Magalhães, Tomás Jorge, Rúben Silva, António Guedes, Diogo Ribeiro, Andreia Meixedo, Araliya Mosleh, Cecília Vale, Pedro Montenegro, Alexandre Cury

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Deep Learning for Weed Detection and Segmentation in Agricultural Crops Using Images Captured by an Unmanned Aerial Vehicle by Josef Augusto Oberdan Souza Silva, Vilson Soares de Siqueira, Marcio Mesquita, Luís Sérgio Rodrigues Vale, Thiago do Nascimento Borges Marques, Jhon Lennon Bezerra da Silva, Marcos Vinícius da Silva, Lorena Nunes Lacerda, José Francisco de Oliveira-Júnior, João Luís Mendes Pedroso de Lima, Henrique Fonseca Elias de Oliveira

“…The data from this manuscript show that deep learning models can generate efficient results for automatic weed detection when trained with a well-labeled and large set. Furthermore, this study demonstrated the great potential of using advanced object segmentation algorithms in detecting weeds in soybean and bean crops.…”
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Optimization of constant-speed control algorithm for high-speed EMUs by XIONG Yan, ZHOU Shangru, XU Qing, HUANG He

“…Additionally, in order to prevent abrupt changes in traction/braking force and rapid switching between traction and braking, additional control functions are incorporated in the PID regulation algorithm, focusing on train impact rates and traction/braking switching. …”
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Reinforcement Learning-Based Robust Vehicle Control for Autonomous Vehicle Trajectory Tracking by Attila Lelkó, Balázs Németh, Péter Gáspár

“…The neural network was trained using the Proximal Policy Optimization algorithm, and the robust control is based on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi mathvariant="script">H</mi><mo>∞</mo></msub></mrow></semantics></math></inline-formula>. …”
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Advancing sustainable mobility: Dynamic predictive modeling of charging cycles in electric vehicles using machine learning techniques and predictive application development by Biplov Paneru, Durga Prasad Mainali, Bishwash Paneru, Sanjog Chhetri Sapkota

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UAVRM-A*: A Complex Network and 3D Radio Map-Based Algorithm for Optimizing Cellular-Connected UAV Path Planning by Yanming Chai, Yapeng Wang, Xu Yang, Sio-Kei Im, Qibin He

“…In recent research on path planning for cellular-connected Unmanned Aerial Vehicles (UAVs), leveraging navigation models based on complex networks and applying the A* algorithm has emerged as a promising alternative to more computationally intensive methods, such as deep reinforcement learning (DRL). …”
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Forecasting of energy consumption rate and battery stress under real-world traffic conditions using ANN model with different learning algorithms by Anbazhagan Geetha, S. Usha, J. Santhakumar, Surender Reddy Salkuti

“…This study examined several input factors for the prediction of vehicle performance. Input conditions were energy management controls, State of Charge (SOC) power train batteries, and ultra-capacitor vehicle models; output metrics included consumption rates, battery loads, and trip distances. …”
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