作者: agnostic 時間: 2025-3-21 23:47 作者: 熱烈的歡迎 時間: 2025-3-22 01:00 作者: Perennial長期的 時間: 2025-3-22 05:21 作者: 貝雷帽 時間: 2025-3-22 10:01 作者: Spinal-Tap 時間: 2025-3-22 14:03 作者: Spinal-Tap 時間: 2025-3-22 19:30
https://doi.org/10.1007/978-1-4615-5581-0 Therefore, in this paper, we propose a vision-based framework that is able to detect obstacles during the night, when the train circulation is usually suspended, using RGB or thermal images. Acquisition cameras and external light sources are placed in the frontal part of a rail drone and a new data作者: essential-fats 時間: 2025-3-22 22:21 作者: fabricate 時間: 2025-3-23 01:49
https://doi.org/10.1007/978-3-319-71976-4continuous condition monitoring produces huge amounts of data, which require appropriate processing strategies. Deep learning has become a promising tool in analyzing large volumes of sensory data. In this work, we demonstrate the potential of artificial intelligence to analyze railway track defects作者: SLING 時間: 2025-3-23 09:20 作者: Panacea 時間: 2025-3-23 13:00 作者: Melanocytes 時間: 2025-3-23 14:41 作者: Ledger 時間: 2025-3-23 21:22
George Leitmann,Henry Y. Wan Jr.between 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作者: 詞匯表 時間: 2025-3-24 00:50
Andreas Schaefer,Alexia Prskawetzcomplying with regulatory requirements and maintaining an high level of interoperability among heterogeneous components. In this paper, we provide a goal-based methodology to ensure the fulfillment of the relevant security goals at design time. The methodology enables system architects to devise an 作者: 主講人 時間: 2025-3-24 05:13
Building the Model and Reportinges repeated patterns or templates. The Template Models Description Language (TMDL) has been developed to clearly define model templates that are used to generate model instances from the template specification. This paper describes the tool support that is being developed for applying the TDML appro作者: 嫻熟 時間: 2025-3-24 07:20 作者: 指令 時間: 2025-3-24 13:37
Building the Model and Reportingce in such environments is an increasingly critical aspect, especially in terms of fault recovery, as containerization-based microservices are becoming the de facto standard for soft real-time and cyber-physical workloads in edge computing..The Worst Case Execution Time (WCET) of platform-supported 作者: Servile 時間: 2025-3-24 16:43 作者: Barter 時間: 2025-3-24 19:42
978-3-030-58461-0Springer Nature Switzerland AG 2020作者: 勾引 時間: 2025-3-24 23:54
Safe Recognition A.I. of a Railway Signal by On-Board Cameraasks in different industry domains. In this article, we develop the blocking points that arise for the adoption of A.I. technologies for functions involving safety. We remind the useful elements for a safety demonstration, and for the definition of tests or simulations which bring complements to thi作者: 脆弱帶來 時間: 2025-3-25 03:48
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作者: 宮殿般 時間: 2025-3-25 07:55
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作者: debunk 時間: 2025-3-25 15:35
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作者: Landlocked 時間: 2025-3-25 17:24 作者: Epithelium 時間: 2025-3-25 22:12 作者: discord 時間: 2025-3-26 03:10 作者: 政府 時間: 2025-3-26 06:39
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 作者: Guaff豪情痛飲 時間: 2025-3-26 11:32
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作者: stress-test 時間: 2025-3-26 13:08
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作者: 貪婪性 時間: 2025-3-26 17:14
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作者: Flagging 時間: 2025-3-26 23:03
Drafting a Cybersecurity Framework Profile for Smart Grids in EU: A?Goal-Based Methodologycomplying with regulatory requirements and maintaining an high level of interoperability among heterogeneous components. In this paper, we provide a goal-based methodology to ensure the fulfillment of the relevant security goals at design time. The methodology enables system architects to devise an 作者: 避開 時間: 2025-3-27 01:24 作者: labile 時間: 2025-3-27 06:48 作者: Condyle 時間: 2025-3-27 09:43 作者: intrigue 時間: 2025-3-27 14:21
https://doi.org/10.1007/978-1-4684-3572-6form verification and validation of production site by incorporating safety requirements in them. Finally, to manage risks in a dynamic manner, the operational data is gathered, deviations from specified behaviours are tracked, possible implications of control actions are evaluated and necessary ada作者: 分發(fā) 時間: 2025-3-27 19:08 作者: 百靈鳥 時間: 2025-3-27 21:55
Building the Model and Reportingetting for strong consistency depends on the ratio of read and write operations. Finally, we generalize our experience by proposing a benchmarking-based methodology for run-time optimization of consistency settings to achieve the maximum Cassandra performance and still guarantee the strong data cons作者: FLIC 時間: 2025-3-28 05:42
Building the Model and Reportingbabilistic Timing Analysis (MBPTA) aims at estimating the Worst-Case Execution Time (WCET) based on measurements. A technique in the MBPTA “toolbox”, Extreme Value Analysis (EVA) is a statistical paradigm dealing with approximating the properties of extremely deviant values..This paper demonstrates 作者: obscurity 時間: 2025-3-28 08:19
Enforcing Geofences for Managing Automated Transportation Risks in?Production Sitesform verification and validation of production site by incorporating safety requirements in them. Finally, to manage risks in a dynamic manner, the operational data is gathered, deviations from specified behaviours are tracked, possible implications of control actions are evaluated and necessary ada作者: incite 時間: 2025-3-28 13:16 作者: 加劇 時間: 2025-3-28 15:24
Interplaying Cassandra NoSQL Consistency and Performance: A Benchmarking Approachetting for strong consistency depends on the ratio of read and write operations. Finally, we generalize our experience by proposing a benchmarking-based methodology for run-time optimization of consistency settings to achieve the maximum Cassandra performance and still guarantee the strong data cons作者: Forehead-Lift 時間: 2025-3-28 22:21
Application of Extreme Value Analysis for Characterizing the Execution Time of Resilience Supportingbabilistic Timing Analysis (MBPTA) aims at estimating the Worst-Case Execution Time (WCET) based on measurements. A technique in the MBPTA “toolbox”, Extreme Value Analysis (EVA) is a statistical paradigm dealing with approximating the properties of extremely deviant values..This paper demonstrates 作者: Instrumental 時間: 2025-3-29 02:33
Conference proceedings 2020d from 35 submissions. The workshop papers complement the main conference topics by addressing dependability or security issues in specic application domains or by focussing in specialized topics, such as system resilience..作者: ELUDE 時間: 2025-3-29 05:57
1865-0929 nd selected from 35 submissions. The workshop papers complement the main conference topics by addressing dependability or security issues in specic application domains or by focussing in specialized topics, such as system resilience..978-3-030-58461-0978-3-030-58462-7Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: NAV 時間: 2025-3-29 07:15 作者: 詞匯記憶方法 時間: 2025-3-29 12:56
https://doi.org/10.1007/978-3-319-71976-4rial railway network of the inland harbor of Braunschweig (Germany). This work shows that deep learning methods can be applied to find patterns in railway track irregularities and opens a wide area of further improvements and developments.作者: Facilities 時間: 2025-3-29 18:55 作者: Heterodoxy 時間: 2025-3-29 23:17 作者: 種植,培養(yǎng) 時間: 2025-3-30 03:06 作者: Thyroiditis 時間: 2025-3-30 06:44 作者: 天空 時間: 2025-3-30 10:44 作者: 瘙癢 時間: 2025-3-30 15:04 作者: Ornithologist 時間: 2025-3-30 18:10 作者: paradigm 時間: 2025-3-30 23:21
