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Artykuły w czasopismach na temat "Autonomous Train"
Guo, Yichen. "Design and research of train-centric autonomous control CBTC system". Applied and Computational Engineering 12, nr 1 (25.09.2023): 260–67. http://dx.doi.org/10.54254/2755-2721/12/20230364.
Pełny tekst źródłaAFANASOV, A., D. LINIK, S. ARPUL, D. BELUKHIN i V. VASYLYEV. "PROSPECTS OF USING AUTONOMOUS ELECTRIC TRAINS WITH ONBOARD STORAGE STORES". Transport systems and transportation technologies, nr 23 (28.07.2022): 46. http://dx.doi.org/10.15802/tstt2022/261652.
Pełny tekst źródłaOh, Sehchan, Kyunghee Kim i Hyeonyeong Choi. "Train interval control and train-centric distributed interlocking algorithm for autonomous train driving control system". Journal of the Korea Academia-Industrial cooperation Society 17, nr 11 (30.11.2016): 1–9. http://dx.doi.org/10.5762/kais.2016.17.11.1.
Pełny tekst źródłaAtherton, Mark, Stuart Hill, David Harrison i Marco Ajovalasit. "Economic and technical feasibility of a robotic autonomous system for train fluid servicing". Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, nr 3 (20.02.2019): 338–50. http://dx.doi.org/10.1177/0954409719830520.
Pełny tekst źródłaHadas, Zdenek, Jan Smilek i Ondrej Rubes. "Energy harvesting from passing train as source of energy for autonomous trackside objects". MATEC Web of Conferences 211 (2018): 05003. http://dx.doi.org/10.1051/matecconf/201821105003.
Pełny tekst źródłaHwang, Jong-Gyu, Sung-Yoon Chae, Byung-Hun Lee i Rag-Gyo Jeong. "Design of Train Driving Control System for SITL-based Autonomous Train Control Simulator". Journal of Korean Institute of Information Technology 21, nr 11 (30.11.2023): 71–79. http://dx.doi.org/10.14801/jkiit.2023.21.11.71.
Pełny tekst źródłaKim, Kyung Min, i Suk-Joon Ko. "Path Search for Autonomous Train via Dial Algorithm". Journal of Korean Institute of Communications and Information Sciences 43, nr 4 (30.04.2018): 775–83. http://dx.doi.org/10.7840/kics.2018.43.4.775.
Pełny tekst źródłaLee, Dong-Jin, Ilmu Byun i Rag-Gyo Jeong. "Integrated Antenna Design for Autonomous Train Control System". Journal of the Korean Society for Railway 27, nr 6 (30.06.2024): 492–502. http://dx.doi.org/10.7782/jksr.2024.27.6.492.
Pełny tekst źródłaKim, Hyunkun, Hyeongoo Pyeon, Jong Sool Park, Jin Young Hwang i Sejoon Lim. "Autonomous Vehicle Fuel Economy Optimization with Deep Reinforcement Learning". Electronics 9, nr 11 (13.11.2020): 1911. http://dx.doi.org/10.3390/electronics9111911.
Pełny tekst źródłaJing, Chunhui, Haohong Dai, Xing Yao, Dandan Du, Kaidi Yu, Dongyu Yu i Jinyi Zhi. "Influence of Multi-Modal Warning Interface on Takeover Efficiency of Autonomous High-Speed Train". International Journal of Environmental Research and Public Health 20, nr 1 (25.12.2022): 322. http://dx.doi.org/10.3390/ijerph20010322.
Pełny tekst źródłaRozprawy doktorskie na temat "Autonomous Train"
Chelouati, Mohammed. "Contributions to safety assurance of autonomous trains". Electronic Thesis or Diss., Université Gustave Eiffel, 2024. http://www.theses.fr/2024UEFL2014.
Pełny tekst źródłaThe deployment of autonomous trains raises many questions and challenges, particularly concerning the required safety level, which must be globally at least equivalent to that of the existing systems, along with how to achieve it. Conventionally, ensuring the safety of a global railway system or a defined subsystem includes analyzing risks and effectively handling dangerous situations. Therefore, for any technical railway system, whether it is conventional, automatic, or autonomous, an acceptable level of safety must be ensured. In the context of autonomous trains, safety challenges include aspects related to the use of artificial intelligence models, the transfer of tasks and responsibilities from the driver to automatic decision-making systems, and issues related to autonomy, such as mode transitions and management of degraded modes. Thus, the safety demonstration methodology for autonomous trains must take into account the risks generated by all these aspects. In other words, it must define all the safety activities (related to the introduction of autonomy and artificial intelligence systems), complementary to conventional safety demonstration. In this context, this dissertation proposes three main contributions towards the development of a safety assurance methodology for autonomous trains. Firstly, we establish a high-level framework for structuring and presenting safety arguments for autonomous trains. This framework is based on a goal-based approach represented by the graphical modeling Goal Structuring Notation (GSN). Then, we propose a model for the situational awareness of the automated driving system of an autonomous train, that integrating the process of dynamic risk assessment. This model enables the automated driving system to perceive, understand, anticipate and adapt its behavior to unknown situations while making safe decisions. This model is illustrated through a case study related to the obstacle detection and avoidance. Finally, we develop a decision-making approach based on dynamic risk assessment. The approach is based on Partially Observable Markov Decision Processes (POMDP) and aims to ensure continuous environmental monitoring to guarantee operational safety, particularly collision prevention. The approach is based on maintaining an acceptable level of risk through continuous estimation and updating of the train's operational state and environmental perception data
Wedberg, Magnus. "Detecting Rails in Images from a Train-Mounted Thermal Camera Using a Convolutional Neural Network". Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138523.
Pełny tekst źródłaBoussik, Amine. "Apprentissage profond non-supervisé : Application à la détection de situations anormales dans l’environnement du train autonome". Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2023. http://www.theses.fr/2023UPHF0040.
Pełny tekst źródłaThe thesis addresses the challenges of monitoring the environment and detecting anomalies, especially obstacles, for an autonomous freight train. Although traditionally, rail transport was under human supervision, autonomous trains offer potential advantages in terms of costs, time, and safety. However, their operation in complex environments poses significant safety concerns. Instead of a supervised approach that requires costly and limited annotated data, this research adopts an unsupervised technique, using unlabeled data to detect anomalies based on methods capable of identifying atypical behaviors.Two environmental surveillance models are presented : the first, based on a convolutional autoencoder (CAE), is dedicated to identifying obstacles on the main track; the second, an advanced version incorporating the vision transformer (ViT), focuses on overall environmental surveillance. Both employ unsupervised learning techniques for anomaly detection.The results show that the highlighted method offers relevant insights for monitoring the environment of the autonomous freight train, holding potential to enhance its reliability and safety. The use of unsupervised techniques thus showcases the utility and relevance of their adoption in an application context for the autonomous train
Bouchama, Hiba Fawzia. "Synthèse d’observateurs continus-discrets pour les systèmes non linéaires : Application au Train Autonome". Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2024. http://www.theses.fr/2024UPHF0005.
Pełny tekst źródłaThis thesis contributes to the collaborative project "Train de Fret Autonome'' led by the SNCF and aims to control autonomous freight trains in order to guarantee reliable and safe autonomous driving in all environmental conditions. In this context, our contributions concern the development of estimators for accurately reconstructing the train position and longitudinal speed under variable wheel-rail adhesion conditions. The major difficulty arises from the fact that on-board odometric sensors provide a measurement of wheel rotation at axle level, but do not directly detect the phenomenon of wheel slippage, resulting in inaccurate estimation of the longitudinal speed of the train. To overcome this problem, it is necessary to make a precise recalibration using the position of the train measured by radio beacons installed on the rail. Nevertheless, this measurement is discrete with a variable sampling period. One of the challenges is to consider train measurements that combine both continuous and aperiodically sampled measurements. Thus, the main theoretical contribution of this thesis is the design of a continuous-discrete observer for a class of multi-input/multi-output systems with continuous noisy outputs and aperiodically sampled outputs. This observer is designed to meet the specifications of the "Autonomous Freight Train" project, in order to estimate train speed under variable adhesion conditions. The performance of this observer is shown in simulation and compared with other approaches to train speed estimation, then validated experimentally via an experimental test program carried out at the Centre d'Essai Ferroviaire of Tronville-en-Barrois
Mosskull, Albin, i Arfvidsson Kaj Munhoz. "Solving the Hamilton-Jacobi-Bellman Equation for Route Planning Problems Using Tensor Decomposition". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289326.
Pełny tekst źródłaAtt optimera färdvägen för flertalet au-tonoma fordon i komplexa trafiksituationer kan leda till effekti-vare trafik. Om man försöker lösa dessa optimeringsproblemcentralt, för alla fordon samtidigt, leder det ofta till algorit-mer som uppvisar The curse of dimensionality, vilket är då beräkningstiden och minnes-användandet växer exponentielltmed antalet fordon. Detta gör många problem olösbara för endasten måttlig mängd fordon. Däremot kan sådana problem hanterasgenom numeriska verktyg så som tensornedbrytning. I det här projektet undersöker vi olika metoder för tensornedbrytningoch motsvarandes algoritmer för att lösa optimala styrproblem,genom att jämföra dessa för ett problem med en känd lösning.Dessutom formulerar vi komplexa trafiksituationer som optimalastyrproblem för att sedan lösa dem. Detta gör vi genom attanvända den bästa tensornedbrytningen och genom att noggrantanpassa kostnadsparametrar. Från dessa resultat framgår det att Sequential Alternating Least Squaresalgoritmen, tillsammans medkanonisk tensornedbrytning, överträffade de andra algoritmersom testades. De komplexa trafiksituationerna kan lösas genomatt ansätta släta kostnadsfunktioner, men det kräver omfattandetestning för att uppnå sådana resultat då numeriska fel lätt kan uppstå som ett resultat av dålig problemformulering.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
Fagerström, Malin. "Autonomous Sensory Meridian Response and State-Trait Anxiety in Adults". Thesis, Umeå universitet, Institutionen för psykologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184030.
Pełny tekst źródłaAutonomous Sensory Meridian Response (ASMR) är ett snabbt växande men understuderatsensoriskt koncept som enligt tidigare studier kan underlätta symptom av exempelvis depression, ångest, smärta och stress samtidigt som det skapar en avslappnande känsla. Upplevelsen börjar med ett triggande audio eller audiovisuellt stimuli som orsakar en fysisk reaktion beskrivet som en pirrig känsla med början vid bakhuvudet och vidare mot kroppens periferi. En studie om ämnet föreslog att ASMR och neuroticism är relaterade. Ångest är en del av neuroticism, varför den här studien undersöker relationen mellan ASMR och State- och Trait-Anxiety (S- och T-Anxiety) för att undersöka hur och på vilket sätt de är relaterade. Forskningsfrågorna var ”Är nivån av State och Trait Anxiety associerad med nivån av ASMRupplevelse?” och ”Finns det specifika ASMR stimuli som är starkare relaterade till State och Trait Anxiety än andra ASMR stimuli?”. Totalt slutförde 35 deltagare enkäterna, av vilka majoriteten var mellan åldrarna 25–34, kvinnor, anställda, sammanlevande och rapporterade att studier på universitet/högskola var deras högsta nivå av utbildning. Resultaten från den här självrapporterings-korrelationsstudien visade att ASMR och T-Anxiety är signifikant negativt korrelerade, men det kunde inte visas att S-Anxiety och ASMR är signifikant korrelerade. De visade också att ett ASMR stimuli, finger flutters, är signifikant negativt korrelerad med TAnxiety. Resterande individuella ASMR stimuli hade ingen signifikant korrelation med vare sig S- eller T-Anxiety. Det här tyder på att ångest kanske inte är den drivande underkategorin i jakt på förklaring till vad som gör ASMR och neuroticism associerade. Det väcker också frågan om ASMR verkligen hade varit en lämplig terapeutisk metod för hantering av hög T-Anxiety. Till sist verkar det som att individuella ASMR stimuli varierar i sin relation till ångest. Viktigt att tillägga är dock att det bara är möjligt att dra slutsatser om urvalet, inte populationen, på grund av storleken på urvalet. Ytterligare studier behövs för att åtminstone verifiera dessa resultat.
Bouibed, Kamel. "Contribution à la gestion de défaillances d’un train de véhicules électriques légers autonomes". Thesis, Lille 1, 2010. http://www.theses.fr/2010LIL10030/document.
Pełny tekst źródłaThis work concerns the design of a supervision system applied to autonomous electric vehicle. Two based model monitoring methods are developed using the nonlinear model of electric vehicle: The first is based on multiple observers to detect and to isolate faults. The second approach is the generation of nonlinear analytical redundancy relations (nonlinear parity space). In addition to the generation of residuals by the method of parity space, differential sliding mode observers are integrated to estimate the successive derivatives of inputs and outputs. To make a train of three vehicles, a longitudinal control (control of the safety space) based on measuring the distance between vehicles is developed. The lateral control is based on a reference path tracking imposed for all vehicles. A steering command is then calculated based on the difference between the measured and the desired trajectory. Finally, some strategies are developed for the reconfiguration of the train of vehicles. The principle of the reconfiguration is the choice of strategy for the train of vehicles according to kind of detected fault
Frenette, Patrick. "Architecture décisionnelle pour la conduite collaborative de véhicules autonomes". Mémoire, Université de Sherbrooke, 2010. http://savoirs.usherbrooke.ca/handle/11143/1548.
Pełny tekst źródłaArmstrong, Lindsay Faye. "Thaw Slump Activity Via Close-range ‘Structure from Motion’ in Time-lapse Using Ground-based Autonomous Cameras". Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36885.
Pełny tekst źródłaPoline, Marie. "Contribution aux méthodes de conception et de gestion des systèmes énergétiques multi-sources par optimisation systémique : application aux trains hybrides électrique autonomes". Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT099/document.
Pełny tekst źródłaIn France, there are two traction modes for railway: the diesel and electric traction. Each mode has its own issues. For diesel, the increasing control of the greenhouse gas emissions imposes to evolve this type of train to a less polluting solution. For electric traction, the energy consumption creates a voltage drop which can cause a traffic slowdown, which will limit the traffic development. The studied solution by SNCF is the hybridization of the train (adding storage system).Thus, these works have the objective to build a method to do the pre-sizing of storage systems embedded in trains. Moreover, to take into account the mutual influence of the sizing and the energy management, this last one is included in the sizing model. An optimization algorithm solves the global model.The method has been developed for the two traction modes (diesel and electric) and the optimization has been made with SQP algorithm (Sequential Quadratic Programming)
Książki na temat "Autonomous Train"
Hedin, Sven Anders. The trail of war: On the track of 'Big Horse' in Central Asia. London: Tauris Parke Paperbacks, 2009.
Znajdź pełny tekst źródłaSnibbe, Scott. How to Train a Happy Mind: A Skeptic's Path to Enlightenment. Watkins Media Limited, 2023.
Znajdź pełny tekst źródłaLustgarten, Abrahm. China's Great Train: Beijing's Drive West and the Campaign to Remake Tibet. Holt & Company, Henry, 2009.
Znajdź pełny tekst źródłaLustgarten, Abrahm. China's Great Train: Beijing's Drive West and the Campaign to Remake Tibet. St. Martin's Griffin, 2009.
Znajdź pełny tekst źródłaWangui, Edna. Adaptation to Current and Future Climate in Pastoral Communities Across Africa. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.604.
Pełny tekst źródłaSangeetha, V., i S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.
Pełny tekst źródłaSullivan, Mark D. Seeking the Roots of Health and Action in Biological Autonomy. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780195386585.003.0010.
Pełny tekst źródłaScerri, Eric, i Grant Fisher, red. Essays in the Philosophy of Chemistry. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780190494599.001.0001.
Pełny tekst źródłaGephart, Werner; Witte, red. The Sacred and the Law: The Durkheimian Legacy. Klostermann, 2017. http://dx.doi.org/10.5771/9783465142942.
Pełny tekst źródłaCzęści książek na temat "Autonomous Train"
Peleska, Jan, Anne E. Haxthausen i Thierry Lecomte. "Standardisation Considerations for Autonomous Train Control". W Lecture Notes in Computer Science, 286–307. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19762-8_22.
Pełny tekst źródłaRey, Jeanne. "“Notice how you feel” and “train your brain”". W The Fabrication of the Autonomous Learner, 91–104. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003379676-7.
Pełny tekst źródłaHassoun, Melissa, Yassine Idel Mahjoub i Damien Trentesaux. "Blockchain Adoption for Autonomous Train: Opportunities and Challenges". W Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, 181–95. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24291-5_15.
Pełny tekst źródłaPotena, Ciro, Daniele Nardi i Alberto Pretto. "Fast and Accurate Crop and Weed Identification with Summarized Train Sets for Precision Agriculture". W Intelligent Autonomous Systems 14, 105–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-48036-7_9.
Pełny tekst źródłaGely, Corentin, Damien Trentesaux i Antoine Le Mortellec. "Maintenance of the Autonomous Train: A Human-Machine Cooperation Framework". W Towards User-Centric Transport in Europe 2, 135–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38028-1_10.
Pełny tekst źródłaBouibed, Kamel, Abdel Aitouche i Mireille Bayart. "Modelling and Control of a Train of Autonomous Electric Vehicles". W Intelligent Robotics and Applications, 126–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10817-4_12.
Pełny tekst źródłaMahtani, Ankur, Nadia Chouchani, Maxime Herbreteau i Denis Rafin. "Enhancing Autonomous Train Safety Through A Priori-Map Based Perception". W Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification, 115–29. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05814-1_8.
Pełny tekst źródłaMezzina, G., M. Barbareschi, Salavatore De Simone, Alessandro Di Benedetto, G. Narracci, C. L. Saragaglia, D. Serra i Daniela De Venuto. "Smart On-Board Surveillance Module for Safe Autonomous Train Operations". W Lecture Notes in Electrical Engineering, 317–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95498-7_44.
Pełny tekst źródłaKokare, Bapu Dada, Anil Kumar Gupta i Sanjay A. Deokar. "Electric Power-Train Pre-fault Detection Using AI with IoT". W Internet of Vehicles and its Applications in Autonomous Driving, 173–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46335-9_11.
Pełny tekst źródłaSebron, Walter, Elaheh Gol-Hashem, Peter Krebs i Hans Tschürtz. "The Impact of Train Station Topologies on Operation of Autonomous People Movers". W Communications in Computer and Information Science, 223–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85521-5_15.
Pełny tekst źródłaStreszczenia konferencji na temat "Autonomous Train"
Naidu, Harikumar, Harshita Chourasia, Praveen Kumar Mannepalli i Pankaj Ramtekkar. "Advancing Train Safety Through Autonomous IoT based Obstacle Detection and Response". W 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/icicet59348.2024.10616334.
Pełny tekst źródłaTrentesaux, Damien, Rudy Dahyot, Abel Ouedraogo, Diego Arenas, Sebastien Lefebvre, Walter Schon, Benjamin Lussier i Hugues Cheritel. "The Autonomous Train". W 2018 13th Annual Conference on System of Systems Engineering (SoSE). IEEE, 2018. http://dx.doi.org/10.1109/sysose.2018.8428771.
Pełny tekst źródłaMatsumoto, Masayuki, i Naohisa Kitamura. "Autonomous decentralized train control technology". W 2009 International Symposium on Autonomous Decentralized Systems (ISADS). IEEE, 2009. http://dx.doi.org/10.1109/isads.2009.5207322.
Pełny tekst źródłaChouchani, Nadia, i Sana Debbech. "ATMO: Autonomous Train Map Ontology". W 11th International Conference on Model-Based Software and Systems Engineering. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011893200003402.
Pełny tekst źródłaYuan, Lei, Weihui Zhao, Chenling Li i Datian Zhou. "Error correction method for train speed measurement using Doppler radar in train control system". W 2013 IEEE Eleventh International Symposium on Autonomous Decentralized Systems (ISADS). IEEE, 2013. http://dx.doi.org/10.1109/isads.2013.6513429.
Pełny tekst źródłaLi, Lulu, Haifeng Song, Jianjun Ma i Hairong Dong. "Description and Analysis of Train-centric Communication based Autonomous Train Control System". W 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022. http://dx.doi.org/10.1109/itsc55140.2022.9922576.
Pełny tekst źródłaMahtani, Ankur, Wael Ben-Messaoud, Abdelmalik Taleb-Ahmed, Smail Niar i Clement Strauss. "Pedestrian Detection and Classification for Autonomous Train". W 2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS). IEEE, 2020. http://dx.doi.org/10.1109/ipas50080.2020.9334938.
Pełny tekst źródłaIshida, Y. "The future of high-speed train". W Proceedings. ISADS 2005. 2005 International Symposium on Autonomous Decentralized Systems. IEEE, 2005. http://dx.doi.org/10.1109/isads.2005.1452101.
Pełny tekst źródłaDeng, Zixing, Haifeng Song, Hua Huang, Yidong Li i Hairong Dong. "Multi-sensor based Train Localization and Data Fusion in Autonomous Train Control System". W 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9327825.
Pełny tekst źródłaHu, Shunding, Hongquan Lin, Xiaohong Lu i Shengmao Xie. "Interoperability Technology Research of Train Autonomous Circumambulate System". W 2022 IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE). IEEE, 2022. http://dx.doi.org/10.1109/icite56321.2022.10101387.
Pełny tekst źródłaRaporty organizacyjne na temat "Autonomous Train"
Wang, Shenlong, i David Forsyth. Safely Test Autonomous Vehicles with Augmented Reality. Illinois Center for Transportation, sierpień 2022. http://dx.doi.org/10.36501/0197-9191/22-015.
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