Titlebook: Dependable Computing - EDCC 2020 Workshops; AI4RAILS, DREAMS, DS Simona Bernardi,Valeria Vittorini,Paolo Masci Conference proceedings 2020

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Audio Events Detection in Noisy Embedded Railway Environmentsposed in the scientific community. Since the beginning of the 2010s, the development of deep learning made it possible to develop these research areas in the railway field included. Thus, this article deals with the audio events detection task (screams, glass breaks, gunshots, sprays) using deep lea

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Development of Intelligent Obstacle Detection System on Railway Tracks for Yard Locomotives Using CNtrated by full-stack technology comprises of hardware construction and software implementation. Original video capture device with double cameras making stereoscopic image recording in the realtime mode has been developed. The novel modified edge detection algorithm recognizes railway tracks and obs

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Artificial Intelligence for Obstacle Detection in Railways: Project SMART and Beyondwithin the H2020 Shift2Rail project SMART. The system software includes a novel machine learning-based method that is applicable to long range obstacle detection, the distinguishing challenge of railway applications. The development of this method used a novel long-range railway dataset, which was g

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政府

UIC Code Recognition Using Computer Vision and LSTM Networks computer vision, to gain high-level understanding from digital images, and LSTM, a specific neural network with relevant performance in optical character recognition. Experimental results show that the proposed method has a good localization and recognition performance in complex scene, to improve

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Deep Reinforcement Learning for Solving Train Unit Shunting Problem with Interval Timingotential to solve the parking and matching sub-problem of TUSP by formulating it as a Markov Decision Process and employing a deep reinforcement learning algorithm to learn a strategy. However, the earlier study did not take into account service tasks, which is one of the key components of TUSP. Ser

stress-test

Enforcing Geofences for Managing Automated Transportation Risks in?Production Sitess generally done during the system design and development phase. However, for automated systems, there is also a need to deal with unknowns and uncertainties during operational phase. This paper focuses on virtual boundaries around geographic zones (i.e., geofences) that can serve as an active count

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Safety Cases for Adaptive Systems of?Systems: State of the Art and Current Challengesbetween these. Ensuring continued safety for adaptive SoS is challenging, because either the multitude of relevant configurations must be assessed at design time, or assessment must done dynamically at run time. The concepts of Modular Safety Cases (MSC) and Dynamic Safety Cases (DSC) might form par