Relevant bibliographies by topics / Smart Railway Network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Smart Railway Network'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Smart Railway Network"

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Ranjith, A., and S. P. Vijayaragavan. "Internet of Things (IoT) Based Automated Calamity Avoidance System for Railway Sectors." Journal of Computational and Theoretical Nanoscience 17, no. 12 (2020): 5399–408. http://dx.doi.org/10.1166/jctn.2020.9433.

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Tanzania is the regional gateway for a number of landlocked neighboring countries. Zambia, Burundi, Malawi, Congo, Rwanda and Uganda all rely to certain extent on the countries transport network, linking them to the global network. Railways are found to be the most effective transport system in Tanzania after-road transport system and it is Critical to long-haul freight along the main transport corridors of Tanzania. Most of the operations at Tanzania Railway Sector are manual system. For instance closing and opening of the gates are not automatic resulting in calamities and delays in the train’s advent. An innovative method to enhance railway safety measures is proposed in this Paper. This research paper proposes mechanisms which operate automatically without any 600073 intervention. Internet of Things (IoT) can be used to increase different features of the rail system. Automation of railways can transform the current legacy approaches and help decrease the railway related accidents considerably. Many challenges are underscored and dealt with in the proposed research. To summate, this paper aims at enhancing rail safety, minimizing accidents at unmanned level crossings, introducing smart railway gates, preventing calamity, and incorporating digital transformation through the use of IoT.
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Kryukov, Andrey, Aleksandr Cherepanov, and Irina Lyubchenko. "Integrated use of Smart Grid technologies in railway traction networks." Proceedings of Irkutsk State Technical University 24, no. 5 (2020): 1041–52. http://dx.doi.org/10.21285/1814-3520-2020-5-1041-1052.

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The purpose of the paper is to develop a methodology for modeling railway power supply systems equipped with a set of devices implemented on the base of smart grid technologies. The research is carried out using the Fazonord software package designed for modeling the modes of railway power supply systems in phase coordinates. The calculation model is implemented for the power supply system of a two-track section with five traction substations. The results obtained show that reliable and high-quality power supply of train traction and non-traction consumers can be ensured on the basis of the integrated use of active Smart Grid elements, such as a phase number converter, active harmonic conditioner, controlled reactive power source, and a distributed generation unit. Computer simulation allows to establish that in the absence of reactive power sources there are noticeable voltage fluctuations on 10 kV buses of non-traction consumers; the asymmetry is approaching the limit of normally acceptable values; disabling of the active filter results in the increase of the total harmonic coefficient of voltages up to 16%; if the entire complex of active devices is available, the high quality of electrical energy is achieved; the phase number converter is robust and features low sensitivity to the errors in parameter setting; voltage deviations caused by the limited variation range of reactive power in the reactive power source are short-term and do not exceed the values acceptable in practice. Thus, on the basis of Smart Grid technologies, distributed generation units can be connected directly to the traction network using a phase number conversion device formed according to the reciprocal Steinmetz circuit. Elimination of harmonic distortions created by rectifier electric locomotives is carried out by means of an active conditioner of higher harmonics. A controlled reactive power source can be used to maintain voltage levels.
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KUSAKABE, Takahiko, Yuya TAKAGI, Takamasa IRYO, and Yasuo ASAKURA. "Estimation of Passenger's Train Choice in Railway Network with Smart Card Ticket System." INFRASTRUCTURE PLANNING REVIEW 26 (2009): 655–62. http://dx.doi.org/10.2208/journalip.26.655.

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Gui, Renzhou, Tongjie Chen, and Han Nie. "In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning." Complexity 2020 (January 21, 2020): 1–25. http://dx.doi.org/10.1155/2020/5192861.

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The coordinated development of smart cities has become the goal of world urban development, and the railway network plays an important role in this progress. This paper proposes a solution that integrates data acquisition, storage, GIS visualization, deep learning, and statistical correlation analysis to deeply analyze the distribution data of companies collected in the past 40 years in the Yangtze River Delta. Through deep learning, we predict the spatial distribution of the company after the opening of the train stations. Through statistical and correlation analysis of the company’s registered capital and quantity, the urban development relationship under the influence of the opening of the railway is explored. Going forward, the use and application of such analysis can be tested for use and application in the context of other smart cities for specific aspects or scale.
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Mo, Baichuan, Zhenliang Ma, Haris N. Koutsopoulos, and Jinhua Zhao. "Capacity-Constrained Network Performance Model for Urban Rail Systems." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (2020): 59–69. http://dx.doi.org/10.1177/0361198120914309.

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This paper proposes a general network performance model (NPM) for monitoring the performance of urban rail systems using smart card data. NPM is a schedule-based network loading model with strict capacity constraints and boarding priorities. It distributes passengers over the network given origin-destination demand, operations, route choice, and effective train capacity. A Bayesian simulation-based optimization method for calibrating the effective train capacity is introduced, which explicitly recognizes that capacity may be different at different stations depending on congestion levels. Case studies with data from the Mass Transit Railway network in Hong Kong are used to validate the model and illustrate its applicability. NPM is validated using survey data on left-behind passengers and exiting passenger flow extracted from smart card data. The use of NPM for performance monitoring is demonstrated by analyzing the spatial-temporal crowding patterns in the system and evaluating dispatching strategies.
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Briso-Rodríguez, César, Ke Guan, Yin Xuefeng, and Thomas Kürner. "Wireless Communications in Smart Rail Transportation Systems." Wireless Communications and Mobile Computing 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6802027.

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Railway, subway, airplane, and other transportation systems have drawn an increasing interest on the use of wireless communications for critical and noncritical services to improve performance, reliability, and passengers experience. Smart transportation systems require the use of critical communications for operation and control, and wideband services can be provided using noncritical communications. High speed train (HST) is one of the best test cases for the analysis of communication links and specification of the general requirements for train control and supervision, passenger communications, and onboard and infrastructure wireless sensors. In this paper, we analyze in detail critical and noncritical networks mainly using the HST as a test case. First, the different types of links for smart rail transportation are described, specifying the main requirements of the transportation systems, communications, and their applications for different services. Then, we propose a network architecture and requirements of the communication technologies for critical and noncritical data. Finally, an analysis is made for the future technologies, including the fifth-generation (5G) communications, millimeter wave (mmWave), terahertz (THz), and satellites for critical and high-capacity communications in transportation.
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Tretyakov, Evgeny. "Advanced methods of transportation and distribution of electrical power in smart power grids of railways." MATEC Web of Conferences 239 (2018): 01010. http://dx.doi.org/10.1051/matecconf/201823901010.

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The relevance of the work is determined by the need to improve the electrical distribution grids of railways on the basis of digital technologies. The article presents advanced methods of transportation and distribution of electric power in smart power grids of railways based on multi-agent control. The analysis of the power supply system for stationary railroad consumers was performed and advanced ways of their development were defined. These methods should provide increased speed, adaptive determination of restrictions on using electric power equipment, management of mode parameters, sectioning and power flow modes in electrical distribution grids, restoration of power supply after emergency events. The method of adaptive control of transportation and distribution of electric energy in the power supply system of stationary railway consumers is developed based on the hierarchical structure of IEC 61850. This method takes into account the coordination of managing and local controllers in the data exchange environment, the control results and the variable area of responsibility of controllers and their division according to their functional purpose based on the multi-agent approach. The method of power flow control was developed to reduce power losses, increase the capacity of transport channels and ensure the restoration of the normal mode of the electric network by reconfiguring it and controlling active elements based on graph theory. The method takes into account the expected daily load curve, limits on the demand for capacity by active consumers and the possibility of a closed mode of electrical network operation through controlled cross-sections. The simulation results presented on the test circuit have showed the feasibility and efficiency of the proposed approaches.
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Liu, Tianyou, Zhenliang Ma, and Haris N. Koutsopoulos. "Unplanned Disruption Analysis in Urban Railway Systems Using Smart Card Data." Urban Rail Transit 7, no. 3 (2021): 177–90. http://dx.doi.org/10.1007/s40864-021-00150-x.

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AbstractMetro system disruptions are a big concern due to their impacts on safety, service quality, and operating efficiency. A better understanding of system performance and passenger behavior under unplanned disruptions is critical for efficient decision making, effective customer communication, and identifying potential improvements. However, few studies explore disruption impacts on individual passenger behavior, and most studies use manually collected survey data. This study examines the potential of using automated collection data to comprehensively analyze unplanned disruption impacts. We propose a systematic approach to evaluate disruption impacts on system performance and individual responses in urban railway systems using automated fare collection (AFC) data. We develop a set of performance metrics to evaluate performance from the perspectives of train operations, information provision (communication), and bridging strategy (shuttle bus services to connect stations impacted by a disruption). We also propose an inference method to quantify the individual response to disruptions (e.g. travel or not, change stations or modes) depending on their trip characteristics with respect to the location and timing of the disruption. The proposed approach is demonstrated using data from a busy metro system. The results highlight the ability of AFC data in providing new insights for the analysis of unplanned disruptions, which are difficult to extract from traditional data collection methods. The case study shows that the disruption impacts are network-wide, and the impacts on passengers continue for a significant amount of time after the incident ended. The behavior highlights the importance of real-time information and the need for timely dissemination.
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Ye, Xi Jun, Tian Feng Zhu, Quan Sheng Yan, and Wei Feng Wang. "Experimental Verification of Decentralized Approach for Modal Identification Based on Wireless Smart Sensor Network." Advanced Materials Research 291-294 (July 2011): 3–11. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.3.

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This paper provides an experimental verification of decentralized approach for modal test and analysis of a 30 meters long railway overpass bridge. 11 Imote2 smart sensor nodes were implemented on the WSSN. In order to compare the identification precision of different topologies, acceleration responses were obtained under centralized and 3 different decentralized topologies. Local modal parameters were estimated by NExT/ERA within each local group; true modes were then distinguished from spurious modes by EMAC and finite-element analysis. In order to estimate global mode shape, a least square method was used for calculating the normalization factor. Then the global mode shapes were determined by normalization factors and local mode shapes. The result demonstrates that the more overlapping nodes in each group, the more accurate the global mode shape will be; the decentralized approach is workable for modal test of large-scale bridge.
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Alawad, Hamad, Min An, and Sakdirat Kaewunruen. "Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations." Applied Sciences 10, no. 15 (2020): 5156. http://dx.doi.org/10.3390/app10155156.

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The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems.
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Dissertations / Theses on the topic "Smart Railway Network"

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Gill, Kuldeep S. "Cognitive Radio Connectivity for Railway Transportation Networks." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/129.

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Reliable wireless networks for high speed trains require a significant amount of data communications for enabling safety features such as train collision avoidance and railway management. Cognitive radio integrates heterogeneous wireless networks that will be deployed in order to achieve intelligent communications in future railway systems. One of the primary technical challenges in achieving reliable communications for railways is the handling of high mobility environments involving trains, which includes significant Doppler shifts in the transmission as well as severe fading scenarios that makes it difficult to estimate wireless spectrum utilization. This thesis has two primary contributions: (1) The creation of a Heterogeneous Cooperative Spectrum Sensing (CSS) prototype system, and (2) the derivation of a Long Term Evolution for Railways (LTE-R) system performance analysis. The Heterogeneous CSS prototype system was implemented using Software-Defined Radios (SDRs) possessing different radio configurations. Both soft and hard-data fusion schemes were used in order to compare the signal source detection performance in real-time fading scenarios. For future smart railways, one proposed solution for enabling greater connectivity is to access underutilized spectrum as a secondary user via the dynamic spectrum access (DSA) paradigm. Since it will be challenging to obtain an accurate estimate of incumbent users via a single-sensor system within a real-world fading environment, the proposed cooperative spectrum sensing approach is employed instead since it can mitigate the effects of multipath and shadowing by utilizing the spatial and temporal diversity of a multiple radio network. Regarding the LTE-R contribution of this thesis, the performance analysis of high speed trains (HSTs) in tunnel environments would provide valuable insights with respect to the smart railway systems operating in high mobility scenarios in drastically impaired channels.
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Book chapters on the topic "Smart Railway Network"

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Chen, Tao, Jie-ru Zhang, Hong-xia Lv, and Jin-shan Pan. "Coordination Evaluation Index of High-Speed Railway Network Capacity." In Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04582-1_28.

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Li, Hai-lian, Meng-kai Lin, and Qi-cai Wang. "Passenger Flow Prediction Model of Intercity Railway Based on G-BP Network." In Green, Smart and Connected Transportation Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0644-4_67.

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Zhao, Feng, Hong-Xia Lv, and Bing Wang. "Research on Comprehensive Evaluation Method of Regional Railway Network Scale." In Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04582-1_42.

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Zhang, Xuepeng, Shengdong Li, Xiucong Li, Xinyi Lin, and Xiaoyuan Lv. "Service Quality Evaluation of Railway Freight Transportation Network Based on Bayes Theory." In Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70730-3_11.

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Wang, Pu, Yingjie Wang, and Pei-fen Pan. "The Analysis Method of Regional Railway Network Capacity Loss Under Emergent Conditions." In Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04582-1_13.

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Liu, Su, Jieying Jiang, and Si Ma. "Research on High-Speed Railway Network Effectiveness Based on Theory of Constraints." In Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04582-1_9.

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Jiang, Jieying, Lin Wang, and Si Ma. "Classification of High-Speed Railway Network Transfer Nodes Based on the Improved Gray Whitenization Weight Function Clustering Method." In Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04582-1_8.

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Ni, Shaoquan, Feiyu Yang, and Miaomiao Lv. "Innovation of Networked Railway Transportation Organization in High-Speed Railway." In Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04582-1_35.

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Canonico, Roberto, Stefano Marrone, Roberto Nardone, and Valeria Vittorini. "A Framework to Evaluate 5G Networks for Smart and Fail-Safe Communications in ERTMS/ETCS." In Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68499-4_3.

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Li, Qingyong Y., Zhangdui D. Zhong, Ming Liu, and Weiwei W. Fang. "Smart Railway Based on the Internet of Things." In Big Data Analytics for Sensor-Network Collected Intelligence. Elsevier, 2017. http://dx.doi.org/10.1016/b978-0-12-809393-1.00014-3.

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Conference papers on the topic "Smart Railway Network"

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Thampibal, Lalita, and Wiwat Vatanawood. "Formalizing Railway Network Using Hierarchical Timed Coloured Petri Nets." In ICIT 2019: IoT and Smart City. ACM, 2019. http://dx.doi.org/10.1145/3377170.3377221.

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Kawamura, Atsushi, Yuji Yoshimitsu, Kouichiro Kajitani, Takeshi Naito, Kaichi Fujimura, and Shunsuke Kamijo. "Smart camera network system for use in railway stations." In 2011 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2011. http://dx.doi.org/10.1109/icsmc.2011.6083647.

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Aydin, Ahmet, Mehmet Umut Salur, and Ilhan Aydin. "Fine-Tuning Convolutional Neural Network Based Railway Damage Detection." In IEEE EUROCON 2021 - 19th International Conference on Smart Technologies. IEEE, 2021. http://dx.doi.org/10.1109/eurocon52738.2021.9535585.

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Velagandula, Sathvik Dev, Nirjhar Dhang, Raja Datta, Soumendu Kumar Ghosh, and Suman Maroju. "Railway bridge health monitoring system using smart wireless sensor network." In WiSec '17: 10th ACM Conference on Security & Privacy in Wireless and Mobile Networks. ACM, 2017. http://dx.doi.org/10.1145/3098243.3106023.

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shankar, Shiva, Mahaveer Penna, Dileep Reddy Bolla, Keshava murthy, J. J. Jijesh, and H. A. Navyashree. "Real Time Image Classification Based Smart Railway Platform Using Convolutional Neural Network." In 2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT). IEEE, 2020. http://dx.doi.org/10.1109/rteict49044.2020.9315555.

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Zhou, Yang, Qiang Sun, and Xiaodong Chen. "LSTM Based Network Flow Forecasting in High-Speed Railway Data Network." In 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS). IEEE, 2019. http://dx.doi.org/10.1109/iucc/dsci/smartcns.2019.00040.

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Hai, Ning, Cai-Zhen Zhang, Yang Yu, and Yan Tang. "Reliability Analysis of Clock Source of Railway Time Synchronization Network Based on SPN." In 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2019. http://dx.doi.org/10.1109/icitbs.2019.00020.

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Kamel, T., Z. Tian, and P. Tricoli. "SMART SOP ARCHITECTURES AND POWER CONTROL MANAGEMENTS BETWEEN LIGHT DC RAILWAY AND LV DISTRIBUTION NETWORK." In The 10th International Conference on Power Electronics, Machines and Drives (PEMD 2020). Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/icp.2021.0980.

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Li, Shengyuan, Peigang Li, Yang Zhang, and Xuefeng Zhao. "Detection of Component Types and Track Damage for High-Speed Railway Using Region-Based Convolutional Neural Networks." In ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/smasis2018-8223.

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High-speed railway plays critical roles in public safety and the country’s economy. Visual detection of components and damages can reflect the health conditions of high-speed railway. Human-based visual inspection is a difficult and time-consuming task and its detection results significantly rely on subjective judgement of human inspectors. Image-based detection methods abandon the weakness of human-based visual inspection. However, in practice, the complex real-world situations, such as lighting and shadow changes, can lead to challenges to the wide adaptability of image process techniques. To overcome these challenges, this paper provides a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based detection method of component types and track damage for high-speed railway. To realize the method, a database including 575 images labeled for three component types and one track damage type of high-speed railway is built. A Faster R-CNN architecture based on ZF-Net is modified, then trained and validated using the built database. The performance of the trained Faster R-CNN is evaluated using 50 new images which are not be used for training process. The results show that the proposed method can indeed detect the component types and track damage for high-speed railway.
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Liu, Chengcheng, Kuan Zhang, Peigang Li, Shengyuan Li, and Xuefeng Zhao. "Feature Recognition and Detection for Common Maintenance Tools Based on Deep Learning." In ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/smasis2018-8266.

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With the rapid development of rail traffic, the importance of railway overhaul is becoming increasingly prominent. Making an inventory on tools is an important step that railway workers must take before and after railway inspection. The tools left on the railway will cause great harm to train safety. To avoid this happening, the commonly used method is manual inventory at present, which is time-consuming, laborious and easily leads to omissions. In order to overcome these shortcomings, this paper proposes a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based method for tools inventory. To realize the method, a Faster R-CNN architecture based on ZF-Net is modified and a database including a large number of images for 10 types of tools is built. Then the Faster R-CNN is trained and validated using the built database. The performance of the trained Faster R-CNN is evaluated using some new images which are not be used for training process. The result shows 95.0325% average precision (AP) ratings for 10 different types of tools and proves the proposed method is effective.
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