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    Traffic speed prediction for intelligent transportation system based on a deep feature fusion model

    Li, Linchao / Qu, Xu / Zhang, Jian et al. | Taylor & Francis Verlag | 2019
    Schlagwörter: deep learning , machine learning

    Flight test flutter prediction using neural networks

    Crowther, W.J. / Cooper, J.E. | Tema Archiv | 2001
    Schlagwörter: Lernprozess

    Design of Reinforcement Learning Parameters for Seamless Application of Adaptive Traffic Signal Control

    El-Tantawy, Samah / Abdulhai, Baher / Abdelgawad, Hossam | Taylor & Francis Verlag | 2014
    Schlagwörter: Reinforcement Learning , Temporal Difference Learning

    Phi-Net: Deep Residual Learning for InSAR Parameters Estimation

    Freier Zugriff
    Sica, Francescopaolo / Gobbi, Giorgia / Rizzoli, Paola et al. | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2020
    Schlagwörter: Residual Learning , Deep Learning

    Les réseaux apprennants à la SNCF ou comment rendre les organisations vivante

    Raynard, Thierry | IuD Bahn | 2014
    Schlagwörter: Betriebliches Lernen

    Using reinforcement learning to minimize taxi idle times

    O’Keeffe, Kevin / Anklesaria, Sam / Santi, Paolo et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning , reinforcement learning

    Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach

    Cheng, Yanqiu / Hu, Xianbiao / Chen, Kuanmin et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep Q-learning , reinforcement learning

    Learning efficient haptic shape exploration with a rigid tactile sensor array

    Freier Zugriff
    Fleer, Sascha / Moringen, Alexandra / Klatzky, Roberta L. et al. | BASE | 2020
    Schlagwörter: Learning , Machine learning algorithms , Machine learning

    A CNN-Based Coherence-Driven Approach for InSAR Phase Unwrapping

    Freier Zugriff
    Sica, Francescopaolo / Calvanese, Francesco / Scarpa, Giuseppe et al. | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2020
    Schlagwörter: Deep Learning

    A cold-start-free reinforcement learning approach for traffic signal control

    Xiao, Nan / Yu, Liang / Yu, Jinqiang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning , reinforcement learning

    Deep Q learning-based traffic signal control algorithms: Model development and evaluation with field data

    Wang, Hao / Yuan, Yun / Yang, Xianfeng Terry et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep reinforcement learning , Q-learning

    Artificial intelligence for traffic signal control based solely on video images

    Jeon, Hyunjeong / Lee, Jincheol / Sohn, Keemin | Taylor & Francis Verlag | 2018
    Schlagwörter: deep learning , reinforcement learning (RL)

    Considerazioni sulla formazione mediante simulatori di condotta

    Capaccioli, Tanya | IuD Bahn | 2006
    Schlagwörter: Betriebliches Lernen

    Warning systems triggered by trains increase flight-initiation times of wildlife

    Backs, Jonathan A.J. / Nychka, John A. / St. Clair, Colleen Cassady | Elsevier | 2020
    Schlagwörter: Learning

    Ausbilden auf hohem Niveau

    Komplexer Lernstoff lässt sich in Blended-Learning-Modulen aufbereiten
    Hofmann, Ursula | IuD Bahn | 2004
    Schlagwörter: Betriebliches Lernen

    Estimating wildlife strike costs at US airports: A machine learning approach

    Altringer, Levi / Navin, Jordan / Begier, Michael J. et al. | Elsevier | 2021
    Schlagwörter: Machine learning

    Practical system for implementing vocational training program for improving train driver skills for coping with abnormal situation

    Omino, Koji / Endoh, Hiroharu | IuD Bahn | 2013
    Schlagwörter: Betriebliches Lernen

    Sound-Based Abnormal Combustion Classification Model for High Compression Ratio, Spark-Ignition Engines Using Mel-Frequency Cepstrum Coefficients and Ensemble Learning Algorithms

    Kim, Seongsu / Kim, Junghwan | Springer Verlag | 2023
    Schlagwörter: Supervised learning , Ensemble learning

    Lerntypen

    Mit einer typengerechten Gestaltung zum optimalen Lernerfolg
    Lex, Monika | IuD Bahn | 2007
    Schlagwörter: Betriebliches Lernen

    Machine learning algorithms in ship design optimization

    Peri, Daniele | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Iterative Learning Control Algorithm for Feedforward Controller of EGR and VGT Systems in a CRDI Diesel Engine

    Min, Kyunghan / Sunwoo, Myoungho / Han, Manbae | Springer Verlag | 2018
    Schlagwörter: Learning control , Iterative learning control

    Machine learning approach to ship fuel consumption: A case of container vessel

    Uyanık, Tayfun / Karatuğ, Çağlar / Arslanoğlu, Yasin | Elsevier | 2020
    Schlagwörter: Machine learning

    Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu

    Tu, Meiting / Li, Wenxiang / Orfila, Olivier et al. | Elsevier | 2021
    Schlagwörter: Machine learning

    Modeling the impact of COVID-19 on transportation at later stage of the pandemic: A case study of Utah

    Gong, Yaobang / Isom, Tanner / Lu, Pan et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Kennzahlen per Intranet

    Becker, Egbert | IuD Bahn | 2004
    Schlagwörter: Betriebliches Lernen

    A robust machine learning structure for driving events recognition using smartphone motion sensors

    Zarei Yazd, Mahdi / Taheri Sarteshnizi, Iman / Samimi, Amir et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Fuel consumption estimation in heavy-duty trucks: Integrating vehicle weight into deep-learning frameworks

    Fan, Pengfei / Song, Guohua / Zhai, Zhiqiang et al. | Elsevier | 2024
    Schlagwörter: Deep learning

    Examining threshold effects of built environment elements on travel-related carbon-dioxide emissions

    Wu, Xinyi / Tao, Tao / Cao, Jason et al. | Elsevier | 2019
    Schlagwörter: Machine learning

    Network-wide traffic signal control based on the discovery of critical nodes and deep reinforcement learning

    Xu, Ming / Wu, Jianping / Huang, Ling et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: deep reinforcement learning

    Decision-making for Connected and Automated Vehicles in Chanllenging Traffic Conditions Using Imitation and Deep Reinforcement Learning

    Hu, Jinchao / Li, Xu / Hu, Weiming et al. | Springer Verlag | 2023
    Schlagwörter: Imitation learning , Deep reinforcement learning

    Non-linear associations between the built environment and the physical activity of children

    Huang, Xiaoyan / Lu, Gaigai / Yin, Jiangbin et al. | Elsevier | 2021
    Schlagwörter: Machine learning

    Learning Drivers’ Behavior to Improve Adaptive Cruise Control

    Rosenfeld, Avi / Bareket, Zevi / Goldman, Claudia V. et al. | Taylor & Francis Verlag | 2015
    Schlagwörter: Machine Learning

    Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network

    Taleongpong, Panukorn / Hu, Simon / Jiang, Zhoutong et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    Examining nonlinear and interaction effects of multiple determinants on airline travel satisfaction

    Gao, Kun / Yang, Ying / Qu, Xiaobo | Elsevier | 2021
    Schlagwörter: Machine learning

    Impact of an electrified parkade on the built environment: An unsupervised learning approach

    Fernández, Julián A. / Herrera, Omar E. / Mérida, Walter | Elsevier | 2019
    Schlagwörter: Unsupervised learning

    Sensitivity analysis of driving event classification using smartphone motion data: case of classifier type, sensor bundling, and data acquisition rate

    Sarteshnizi, Iman Taheri / Tavakkoli Khomeini, Farbod / Khedri, Borna et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Characterizing aircraft wake vortex position and strength using LiDAR measurements processed with artificial neural networks

    Freier Zugriff
    Wartha, Niklas Louis / Stephan, Anton / Holzäpfel, Frank et al. | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2022
    Schlagwörter: Machine Learning

    Nonlinear effects of the built environment on metro-integrated ridesourcing usage

    Jin, Tanhua / Cheng, Long / Zhang, Xucai et al. | Elsevier | 2022
    Schlagwörter: Machine learning

    Efficient Exploitation of Existing Corporate Knowledge in Conceptual Ship Design

    Erikstad, Stein Ove / NTNU | Taylor & Francis Verlag | 2007
    Schlagwörter: learning

    An automatic methodology to measure drivers’ behavior in public transport

    Catalán, Hernán / Lobel, Hans / Herrera, Juan Carlos | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Systematische Wissensvermittlung mit neuen Medien in der Fortbildung

    E-Learning bei der Bahn
    Pointner, Martin | IuD Bahn | 2005
    Schlagwörter: Betriebliches Lernen

    Kreative Seminarmethoden

    Wie Sie den Methodeneinsatz im Training zielgerichtet, effektiv und teilnehmeraktivierend gestalten
    Sander, Silke / Lex, Monika | IuD Bahn | 2005
    Schlagwörter: Betriebliches Lernen

    Using kinematic driving data to detect sleep apnea treatment adherence

    McDonald, Anthony D. / Lee, John D. / Aksan, Nazan S. et al. | Taylor & Francis Verlag | 2017
    Schlagwörter: machine learning

    Reinforcement learning-enabled genetic algorithm for school bus scheduling

    Köksal Ahmed, Eda / Li, Zengxiang / Veeravalli, Bharadwaj et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: reinforcement learning

    Using explainable machine learning to understand how urban form shapes sustainable mobility

    Wagner, Felix / Milojevic-Dupont, Nikola / Franken, Lukas et al. | Elsevier | 2022
    Schlagwörter: Explainable machine learning

    Modeling time-of-day car use behavior: A Bayesian network approach

    Li, Dawei / Miwa, Tomio / Morikawa, Takayuki | Elsevier | 2016
    Schlagwörter: Machine learning

    Machine learning estimates of plug-in hybrid electric vehicle utility factors

    Goebel, David / Plötz, Patrick | Elsevier | 2019
    Schlagwörter: Machine learning

    Chancen für die Zukunft durch Bildung

    Berufsbegleitende Fortbildung
    Hebding, Marion | IuD Bahn | 2005
    Schlagwörter: Betriebliches Lernen

    Asynchronous n-step Q-learning adaptive traffic signal control

    Genders, Wade / Razavi, Saiedeh | Taylor & Francis Verlag | 2019
    Schlagwörter: reinforcement learning

    Projektausbildung in der Berufsausbildung

    Gewinnt immer mehr an Bedeutung
    Krause, Andrea | IuD Bahn | 2004
    Schlagwörter: Betriebliches Lernen

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