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3. Neural Network-Based GNSS Localization Error Estimation Method

Author

Li, Xinyi, Cai, Baigen, Lu, Debiao, Liu, Jiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yang, Changfeng, editor, and Xie, Jun, editor

Published
2022
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4. GNSS for Train Localization Trajectory Generation Featuring Depth-First-Search Method

Author

Lu, Jiahao, Lu, Jie, Lu, Debiao, Cai, Baigen, Zhang, Wenbiao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yang, Changfeng, editor, and Xie, Jun, editor

Published
2022
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5. Digital Track Map Aided Multi-sensor Fusion for Train Occupancy Identification in Complicated Track Sections

Author

Yang, Tao, Lu, Debiao, Cai, Baigen, Liu, Jiang, Xiao, Yu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Yang, Changfeng, editor, and Xie, Jun, editor

Published
2021
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6. Train Localization Environmental Scenario Identification Using Features Extracted from Historical Data

Author

Zhang, Tao, Cai, Baigen, Lu, Debiao, Wang, Jian, Xiao, Yu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Yang, Changfeng, editor, and Xie, Jun, editor

Published
2021
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7. Experimental investigation on failure mechanisms of hybrid steel/GFRP hat‐shaped beams under bending loading.

Author

Duan, Libin, Lu, Debiao, Liu, Xing, Zhang, Yu, Xu, Wei, Du, Zhanpeng, and Jiang, Haobing

Subjects
*COMPOSITE structures, *STRAIN rate, *AUTOMOBILE safety, *TRAFFIC accidents, *CRACK propagation
Abstract

The bending failure mechanism and energy absorption characteristics of hybrid metal/composite structures are the keys to the side collision safety of automobiles. The purpose of this paper is to investigate the bending behavior and failure mechanism of hybrid Steel/GFRP hat‐shaped beams under three‐point bending. Firstly, both quasi‐static and dynamic three‐point bending tests were conducted on Steel, GFRP and hybrid Steel/GFRP hat‐shaped beams. Then, the effect of layups and loading speeds on the bending failure behaviors of GFRP hat‐shaped beams were investigated, and the five stages of the bending process of GFRP hat‐shaped beams were exposed. Finally, the failure mechanism of hybrid Steel/GFRP hat‐shaped beams under both quasi‐static and dynamic three‐point bending were investigated. It is found that the failure mechanism is related to the interaction effect between the Steel and GFRP parts in the hybrid Steel/GFRP hat‐shaped beam. In the bending failure process, the crack propagation in GFRP parts is delayed by the plastic deformation of the connected Steel parts, which improves the energy absorption and load carrying capacities of the hybrid Steel/GFRP hat‐shaped beam, and this improvement in the whole bending process is quantitatively exposed in this work. Besides, it is found that with the same impact energy, the load carrying capacity of the hybrid Steel/GFRP hat‐shaped beam can be increased by the loading roll diameter increasing. Highlights: A bending process with five stages of GFRP hat‐shaped beams is exposed.The loading speed increases impact loads due to the strain rate strengthening.The interaction between Steel and GFRP improves the load carrying capacity.The load carrying improvement is due to crack propagation delay in GFRP parts.The greater loading roll diameter increases the load carrying capacity. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Application of DBN for Assessment of Railway Intelligent Signal System Reliability

Author

Li, Zhengjiao, Cai, Bai-gen, Li, Shaobin, Liu, Jiang, Lu, Debiao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Samad, Tariq, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Limin, editor, Qin, Yong, editor, Suo, Jianguo, editor, Feng, Jianghua, editor, Diao, Lijun, editor, and An, Min, editor

Published
2018
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15. Impact and Protection of GNSS Interference: Resilient Positioning in Next Generation Satellite-Based Railway Train Control

Author

Vidal Fonteles Thatyane, Cai Bai-gen, Amuthalakshmi Sivaperuman S., Lassed Somia, Subramani Arunkumar, Tahir Ansari Mohammed, da Silva Severino Lima Jorgiane, K. Zettl Uwe, Kerkatou Wafa, A Prabakaran, Hamdouche Souad, Saquib Hasnain M., Zehani Lamia, Hoffmann Frank, Osvaldo Silva Campos João, Khatoon Saima, Benayache Fadila, Morais Ribeiro da Silva Larissa, Wang Jian, Venkataraman Krishnan, Benayache Samir, Werner Peter, Sain Avtar, Zama Djamila, Teixeira de Figueiredo EvâniaAltina, Ramalakshmi Natarajan N., A. Vijayalakshmi Mookambika, Hilgeroth Andreas, Devi Muraleedharan Usha, Sen Priyankar, Erbênia Pereira Mendes Ana, Khurram Hafiz Aurangzeb, Maria Pontes Silva Ricardo Nágila, Lu Debiao, Farooq Shaikh Mohd., Gebhardt Marcel, Kalam Nida, Kropp Peter, Henrique Machado de Sousa Paulo, S. Sreekala, Liu Jiang, Boumaza Ouahiba, and de Oliveira Souza Tobias

Subjects
Interference (communication), Computer science, GNSS applications, Real-time computing, Control (management), General Earth and Planetary Sciences, Satellite, General Environmental Science
Abstract

Background: The Global Navigation Satellite System (GNSS) has great potentials in next-generation railway train control systems. Considering the fail-safe characteristics of train control, the threat from GNSS interference may result in an increasing likelihood of outages of train positioning or even safety risks to the railway system. Objective: The interference protection solutions are investigated and demonstrated for achieving the resilient train positioning using GNSS. Methods: This paper describes the main types of GNSS interference and investigates the impact on Location Determination Unit (LDU) in the GNSS-based train control system. Specific architectures and solutions for interference detection and protection in both the position domain and measurement domain are presented. Results: Interference injection simulations are performed with both the GNSS spoofing and jamming signals, which evaluate the effects of interferences and demonstrate the protection performance of the presented solutions under GNSS attack scenarios. Conclusion: The interference protection solutions within both the position domain and measurement domain are effective and significant to mitigate the effects from the GNSS interference, which enables resilient train positioning to achieve safe train operation.

Published
2021
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44. Real-time reliability evaluation method for train-ground communication subsystem based on dynamic Bayesian networks

Author

Liu Jiang, Wang Jian, Shangguan Wei, Chen Bin, Cai Bai-gen, Jiang Wei, and Lu Debiao

Subjects
Fault tree analysis, Structure chart, Computer science, 020209 energy, Reliability (computer networking), Control system, Real-time computing, 0202 electrical engineering, electronic engineering, information engineering, Inference, Bayesian network, 02 engineering and technology, Fault (power engineering), Dynamic Bayesian network
Abstract

T rain-ground communication subsystem is a key subsystem in high-speed train control system and plays an important role during the operation of high-speed train. So the reliability of train-ground communication subsystem should be studied in depth. The dynamic fault tree model for train-ground communication subsystem that is modeled based on the structure chart of the subsystem is transformed to Dynamic Bayesian networks. After this, probability of the top event can be obtained by inference algorithm of Bayesian network, and the probability of sub-equipment's failure under the premise of system's failure will be obtained through reverse inference algorithm, which can provide reference information for fault location. The simulations reveal that the reliability of train-ground communication subsystem at any time can be obtained by Dynamic Bayesian Networks method, so we can perform real-time reliability evaluation on train-ground communication subsystem.

Published
2016
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48. GNSS for Train Localisation Performance Evaluation and Verification

Author

Lu, Debiao and Schnieder, Eckehard

Subjects
doctoral thesis, ddc:6, ddc:62, ddc:625
Abstract

Global Navigation Satellite Systems (GNSS) are potentially applicable for various railway applications, especially the safety-related applications such as train localisation for the purpose of train control. In order to integrate GNSS for train localisation, a trustable stand-alone GNSS-based localisation unit should be developed. Then to comply with EN 50126 (reliability, availability, maintainability, and safety; RAMS) standards, the demonstration of GNSS quality of service (QoS) should be evaluated in consistent with RAMS. However there are currently no appropriate performance evaluation methods on GNSS for railway safety-related applications. This dissertation identifies the required performance for train localisation in consideration of GNSS QoS and railway RAMS. The common and different properties of the performance are analysed in detail using consistent attribute hierarchy structures based on UML class diagram. Then formalised performance requirements are proposed quantitatively on four properties (accuracy, reliability, availability, and safety integrity). After that, the evaluation and verification methodologies are introduced. The evaluation methodology is using a reference measurement system for GNSS receiver measured train location accuracy identification, and a stochastic Petri net (SPN) model for GNSS receiver measured train location accuracy categorisation. The SPN model illustrates the GNSS receiver measured train locations into three states (up state, degraded state, and faulty state). Then the four proposed properties are allocated and estimated formally using the three states in the SPN model. The verification methodology is used to verify the GNSS receiver measured train location in real time based on a localisation unit. The GNSS receiver measured train locations are verified using hypothesis testing methods based on the accurate digital track map provided beforehand. Then train location estimation from the localisation unit is verified according to the mileage of the train. With the verified train location estimation from the localisation unit, the corresponding safety margin for each train location is calculated. The data for evaluation and verification methodologies are collected from a test train running on a railway track in High Tatra Mountains. The results show an approach of the possible certification procedure for the GNSS receivers in railway safety-related applications., Globales Satellitennavigationssystem (GNSS) können für verschiedene Anwendungen im Schienenverkehr, vor allem für sicherheitsrelevante Anwendungen wie Zugortung zum Zweck der Zugsicherung gestützt werden. Um GNSS für Zugortung zu integrieren, muss eine eigenständige satellitenbasierte Ortungseinheit entwickelt werden. Um die Entwicklung in Einklang mit EN 50126 (Überlebensfähigkeit, Verfügbarkeit, Instandhaltbarkeit, und Sicherheit; RAMS) durchzuführen, muss der Nachweis der Güte von GNSS (Quality of Service; QoS) entsprechend in Einklang mit dieser Norm bewertet werden. Allerdings gibt es zurzeit keine RAMS Bewertungsverfahren für satellitenbasierte sicherheitsrelevante Anwendungen im Schienenverkehr. Diese Dissertation identifiziert die notwendigen Anforderungen für die Zugortung unter Berücksichtigung der Güte von GNSS und den bestehenden Normen bezüglich RAMS im Schienenverkehr. Die gemeinsamen und unterschiedlichen Eigenschaften der Anforderungen werden detailliert mit Nutzung einer Attributhierarchie basierend auf UML-Klassendiagrammen dargestellt. Danach werden formalisierte Leistungsanforderungen quantitativ für vier Eigenschaften (Genauigkeit, Zuverlässigkeit, Verfügbarkeit und Sicherheitsintegrität) vorgeschlagen. Darauf aufbauend werden die Bewertungs- und Verifikations- Methoden eingeführt. Die Bewertungsmethode nutzt ein Referenzmesssystem zur Identifikation der Zugortungsgenauigkeit der GNSS Empfänger und ein stochastischen Petri-Netz-Modell (SPN-Modell) für die Kategorisierung der GNSS Empfänger Zugortmessungen. Das SPN-Modell veranschaulicht die GNSS Empfänger Zugortmessungen in drei Zuständen (up state, degraded state, faulty state). Dann werden die vier vorgeschlagenen Eigenschaften zugeordnet und formal mit Nutzung der drei Zustände im SPN-Modell geschätzt. Die Verifikationsmethode wird verwendet, um die GNSS Empfänger Zugortmessungen in Echtzeit zu verifizieren. Die GNSS Empfänger Zugortmessungen werden mit einer Hypothesentestmethode auf der Grundlage der genauen digitalen Streckenkarte verifiziert. Mit der verifizierten geschätzten Zugortmessung wird der resultierende Sicherheitsbereich für jeden Zugort berechnet. Die Daten für die Auswertungs- und Verifikationsmethoden wurden von einem Zug im Regelbetrieb auf einer Eisenbahnstrecke in der Hohen Tatra gesammelt. Die Ergebnisse dieser Arbeit zeigen einen Ansatz der möglichen Zertifizierungsverfahren für die GNSS-Empfänger für sicherheitsrelevante Anwendungen im Schienenverkehr.

Published
2014
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