Hasil untuk "Railroad engineering and operation"

Diego Firmenich, Leandro Antonelli, Bruno Pazos et al.

User stories are one of the most widely used artifacts in the software industry to define functional requirements. In parallel, the use of high-fidelity mockups facilitates end-user participation in defining their needs. In this work, we explore how combining these techniques with large language models (LLMs) enables agile and automated generation of user stories from mockups. To this end, we present a case study that analyzes the ability of LLMs to extract user stories from high-fidelity mockups, both with and without the inclusion of a glossary of the Language Extended Lexicon (LEL) in the prompts. Our results demonstrate that incorporating the LEL significantly enhances the accuracy and suitability of the generated user stories. This approach represents a step forward in the integration of AI into requirements engineering, with the potential to improve communication between users and developers.

Xin Wang, Junyan Dai, Xiang Liu

Bianca Trinkenreich, Fabio Calefato, Geir Hanssen et al.

The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range from viewing LLMs as mere productivity tools to considering them revolutionary forces, we argue that the SE research community must proactively engage with and shape the integration of LLMs into research practices, emphasizing human agency in this transformation. As LLMs rapidly become integral to SE research - both as tools that support investigations and as subjects of study - a human-centric perspective is essential. Ensuring human oversight and interpretability is necessary for upholding scientific rigor, fostering ethical responsibility, and driving advancements in the field. Drawing from discussions at the 2nd Copenhagen Symposium on Human-Centered AI in SE, this position paper employs McLuhan's Tetrad of Media Laws to analyze the impact of LLMs on SE research. Through this theoretical lens, we examine how LLMs enhance research capabilities through accelerated ideation and automated processes, make some traditional research practices obsolete, retrieve valuable aspects of historical research approaches, and risk reversal effects when taken to extremes. Our analysis reveals opportunities for innovation and potential pitfalls that require careful consideration. We conclude with a call to action for the SE research community to proactively harness the benefits of LLMs while developing frameworks and guidelines to mitigate their risks, to ensure continued rigor and impact of research in an AI-augmented future.

Md Saeed Siddik, Hao Li, Cor-Paul Bezemer

Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 146 relevant publications from the DBLP Computer Science Bibliography up to the end of 2024, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 64 of the 146 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.

Hein de Wilde, Ali Mohammed Mansoor Alsahag, Pierre Blanchet

Railroad traffic disruption as a result of leaf-fall cost the UK rail industry over 300 million per year and measures to mitigate such disruptions are employed on a large scale, with 1.67 million kilometers of track being treated in the UK in 2021 alone. Therefore, the ability to anticipate the timing of leaf-fall would offer substantial benefits for rail network operators, enabling the efficient scheduling of such mitigation measures. However, current methodologies for predicting leaf-fall exhibit considerable limitations in terms of scalability and reliability. This study endeavors to devise a prediction system that leverages specialized prediction methods and the latest satellite data sources to generate both scalable and reliable insights into leaf-fall timings. An LSTM network trained on ground-truth leaf-falling data combined with multispectral and meteorological satellite data demonstrated a root-mean-square error of 6.32 days for predicting the start of leaf-fall and 9.31 days for predicting the end of leaf-fall. The model, which improves upon previous work on the topic, offers promising opportunities for the optimization of leaf mitigation measures in the railway industry and the improvement of our understanding of complex ecological systems.

Morgan M. Fong, Hilary Egan, Marc Day et al.

Background: Harnessing advanced computing for scientific discovery and technological innovation demands scientists and engineers well-versed in both domain science and computational science and engineering (CSE). However, few universities provide access to both integrated domain science/CSE cross-training and Top-500 High-Performance Computing (HPC) facilities. National laboratories offer internship opportunities capable of developing these skills. Purpose: This student presents an evaluation of federally-funded postgraduate internship outcomes at a national laboratory. This study seeks to answer three questions: 1) What computational skills, research skills, and professional skills do students improve through internships at the selected national laboratory. 2) Do students gain knowledge in domain science topics through their internships. 3) Do students' career interests change after these internships? Design/Method: We developed a survey and collected responses from past participants of five federally-funded internship programs and compare participant ratings of their prior experience to their internship experience. Findings: Our results indicate that participants improve CSE skills and domain science knowledge, and are more interested in working at national labs. Participants go on to degree programs and positions in relevant domain science topics after their internships. Conclusions: We show that national laboratory internships are an opportunity for students to build CSE skills that may not be available at all institutions. We also show a growth in domain science skills during their internships through direct exposure to research topics. The survey instrument and approach used may be adapted to other studies to measure the impact of postgraduate internships in multiple disciplines and internship settings.

Daniel N. Wilke

This paper introduces Design for Sensing and Digitalisation (DSD), a new engineering design paradigm that integrates sensor technology for digitisation and digitalisation from the earliest stages of the design process. Unlike traditional methodologies that treat sensing as an afterthought, DSD emphasises sensor integration, signal path optimisation, and real-time data utilisation as core design principles. The paper outlines DSD's key principles, discusses its role in enabling digital twin technology, and argues for its importance in modern engineering education. By adopting DSD, engineers can create more intelligent and adaptable systems that leverage real-time data for continuous design iteration, operational optimisation and data-driven predictive maintenance.

V. I. Marshev, I. N. Voronin, D. P. Markov

Introduction. The paper considers equipping rolling stock with new lightweight wheel stops of increased reliability, as well as the use of the drag shoes on space intervals, including abnormal situations.Materials and methods. The authors performed field tests to compare the performance of lightweight wheel stops TM 37.10.2016 and hump drag shoes 8739.00. The paper determined the tractive force for moving a empty car train and loaded car train equipped with drag shoes.Results. The paper analyses the securing of railway rolling stock with wheel stops on space intervals with different gradients and different weights of trailing rolling stock. The paper describes the developed methodology of emergency withdrawal of a multiple unit rolling stock with a wedged wheel pair using wheel stops and determines the ultimate sliding distance of the wedged wheel pair compared to the hump drag shoes. The paper established that withdrawing the electric rolling stock from the space interval with the wedged wheel pair requires 4 times more wheel stops than the hump drag shoes.Discussion and conclusion. The authors shown that traction rolling stock permits the practical replacement of hump drag shoes 8739.00 currently in use with wheel stops TM 37.10.2016. The braking characteristics of wheel stops and hump drag shoes only differ during the short initial period of paint abrasion on the rubbing surface of the wheel stop skids. Since the drag shoes for rolling stock are intended for emergency, their rubbing surface must not be painted.

YANG Gang, KONG Guowei, SHEN Xin

Metros are predominantly equipped with low-elasticity overhead rigid catenaries, which can easily lead to poor pantograph-catenary relationship. In recent years, abnormal wear has occurred frequently between the pantographs and catenaries in metro systems, but feasible suppression methods are still lacking due to an unclear mechanisms behind this wear. Metro trains often undergo vertical vibrations with relatively large amplitudes, particularly in areas affected by abnormal track conditions, such as track bed settlement, intersections with roads, irregularities at rail joints, and small vertical curves. These intense vibrations impose significant excitations on the pantographs, resulting in sharp changes in contact force of the pantograph-catenary, which leads to declines in the current-receiving quality of the pantograph-catenary systems and contributes to abnormal wear of the carbon contact strips. In the present study, a dynamics model was constructed to simulate rigid catenary-pantograph systems on metros. The influence of abnormal track excitations on the current-receiving quality of pantographs was analyzed through numerical calculations method. Additionally, feedforward/feedback control strategies were designed for pantographs. A comparative analysis was then conducted to evaluate the effects of several specific control techniques on the current-receiving quality of pantographs, including feedforward control, fuzzy feedback control, and feedforward/feedback integrated control. The results show that: (1) Abnormal track excitations significantly affected the degree of fluctuations in pantograph-catenary contact forces; (2) The feedforward control strategy accurately and effectively compensated for abnormal track excitations; and (3) The feedforward/feedback integrated control effectively reduced the degree of fluctuations in pantograph-catenary contact forces, thereby effectively improving the quality of current-receiving through pantograph-catenary contact while inhibiting mechanical and electrical wear in the pantograph-catenary systems.

Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar et al.

This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.

Juliusz Engelhardt

Abstract: The subject of the article is the development of the railway infrastructure of the Zachodniopomorskie Voivodeship in the period of socio-economic transformation. After an introduction containing a short historical outline, a significant regression of the railway infrastructure in Western Pomerania in the first phase of the transformation period covering the years 1990 - 2004 was indicated. the EU budget perspective 2007-2013 and investments implemented in this period under the Regional Operational Programme. The basic documents concerning the transport policy of the Zachodniopomorskie Voivodship from 2002 and 2010, which defined the needs of the region in the development of railway infrastructure, were also indicated. In the summary of the article, the advantages and disadvantages of the existing railway network in Western Pomerania were pointed out, with the disadvantages clearly determining the directions of past investments in this area of transport infrastructure. Keywords: Transport policy; Railway infrastructure; Line and station investments

Varun Kumar, Somdatta Goswami, Daniel J. Smith et al.

We develop a data-driven deep neural operator framework to approximate multiple output states for a diesel engine and generate real-time predictions with reasonable accuracy. As emission norms become more stringent, the need for fast and accurate models that enable analysis of system behavior have become an essential requirement for system development. The fast transient processes involved in the operation of a combustion engine make it difficult to develop accurate physics-based models for such systems. As an alternative to physics based models, we develop an operator-based regression model (DeepONet) to learn the relevant output states for a mean-value gas flow engine model using the engine operating conditions as input variables. We have adopted a mean-value model as a benchmark for comparison, simulated using Simulink. The developed approach necessitates using the initial conditions of the output states to predict the accurate sequence over the temporal domain. To this end, a sequence-to-sequence approach is embedded into the proposed framework. The accuracy of the model is evaluated by comparing the prediction output to ground truth generated from Simulink model. The maximum $\mathcal L_2$ relative error observed was approximately $6.5\%$. The sensitivity of the DeepONet model is evaluated under simulated noise conditions and the model shows relatively low sensitivity to noise. The uncertainty in model prediction is further assessed by using a mean ensemble approach. The worst-case error at the $(μ+ 2σ)$ boundary was found to be $12\%$. The proposed framework provides the ability to predict output states in real-time and enables data-driven learning of complex input-output operator mapping. As a result, this model can be applied during initial development stages, where accurate models may not be available.

V. L. Nezevak

Introduction. The article discusses the energy parameters of electric power storage systems for DC and AC traction power supply systems. The purpose of the research is to evaluate the energy parameters of electric power storage systems located within the inter-substation zone boundaries of the AC traction power supply system at the 25 kV voltage.Materials and methods. The author used the methods of modeling, statistics and the experimental results processing. Moreover, the paper presented the parameter estimation of the energy accumulation system on the basis of the traction calculations for the AC rolling stock. In addition, the author made two variants of calculations — in the presence and the absence of the electric storage system. The researcher also made the simulation of the electric storage system for the voltage source connected to the sectioning post busbars.Results. Using the simulation modeling, the author presents active and reactive power graphs of the inter-substation zone boundaries in the active sectioning post operation conditions. Therefore, the article demonstrates the graph of the expected charge, the discharge depth calculations for the nominal energy capacity and the required charging features, which guarantee the charge restoration of the accumulation system to the initial level.Discussion and conclusion. The author offers the comparative assessment of the electric storage systems for the traction power supply with the DС voltage of 3 kV and the AC voltage of 25 kV. The research demonstrates the reducing potential of the nominal accumulation parameters.

Qingxiang Zhou, Fang Liu, Jingming Li et al.

Purpose – This study aims to solve the problem of weld quality inspection, for the aluminum alloy profile welding structure of high-speed train body has complex internal shape and thin plate thickness (2–4 mm), the conventional nondestructive testing method of weld quality is difficult to implement. Design/methodology/approach – In order to solve this problem, the ultrasonic creeping wave detection technology was proposed. The impact of the profile structure on the creeping wave detection was studied by designing profile structural test blocks and artificial simulation defect test blocks. The detection technology was used to test the actual welded test blocks, and compared with the results of X-ray test and destructive test (tensile test) to verify the accuracy of the ultrasonic creeping wave test results. Findings – It is indicated that that X-ray has better effect on the inspection of porosities and incomplete penetration defects. However, due to special detection method and protection, the detection speed is slow, which cannot meet the requirements of field inspection of the welding structure of aluminum alloy thin-walled profile for high-speed train body. It can be used as an auxiliary detection method for a small number of sampling inspection. The ultrasonic creeping wave can be used to detect the incomplete penetration welds with the equivalent of 0.25 mm or more, the results of creeping wave detection correspond well with the actual incomplete penetration defects. Originality/value – The results show that creeping wave detection results correspond well with the actual non-penetration defects and can be used for welding quality inspection of aluminum alloy thin-wall profile composite welding joints. It is recommended to use the echo amplitude of the 10 mm × 0.2 mm × 0.5 mm notch as the criterion for weld qualification.

L. A. Bull, D. Di Francesco, M. Dhada et al.

A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilising an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different sub-groups, representing (i) use-type, (ii) component, or (iii) operating condition. Specifically, domain expertise is exploited to constrain the model via assumptions (and prior distributions) allowing the methodology to automatically share information between similar assets, improving the survival analysis of a truck fleet and power prediction in a wind farm. In each asset management example, a set of correlated functions is learnt over the fleet, in a combined inference, to learn a population model. Parameter estimation is improved when sub-fleets share correlated information at different levels of the hierarchy. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The statistical correlations enable knowledge transfer via Bayesian transfer learning, and the correlations can be inspected to inform which assets share information for which effect (i.e. parameter). Both case studies demonstrate the wide applicability to practical infrastructure monitoring, since the approach is naturally adapted between interpretable fleet models of different in situ examples.

Zhilu Lai, Wei Liu, Xudong Jian et al.

The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems (e.g., civil or mechanical structures), which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. Neural Ordinary Differential Equations -- Neural ODEs are exploited as the deep learning operator. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed Neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the abstract mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via physics-informed Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigen-analysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to outperform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, i.e., the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.

Hongbo LI, Rongjun DING, Chao ZHANG et al.

The new round of scientific and technological revolution has propelled the informatization of the urban rail transit industry into a stage of intelligent development, and hence the traditional construction mode and operation service mode have been gradually changed. However, due to diverse factors such as geographic environment, level of economic development, and population density, the development of rail transit in various cities in China is uneven. In order to promote the healthy and orderly construction of informatization and intelligence of the urban rail transit industry, top-level design is needed to coordinate development strategies, clarify construction goals, determine key tasks, and plan implementation paths. The advanced information and communication technology was used to realize real-time data interaction among traction substations, bidirectional converters, trains, signal system, and energy management and control system (ECMS) in this paper. From the perspective of traction grid source and load matching, energy utilization, power quality, and system security, an integrated, collaborative and shared urban rail transit smart traction power grid was built to implement systematical energy dispatch between trains and substations, load forecasting, fault location, and train operation plan optimization. Taking an actual metro line 2 of a certain city as an example, the effectiveness of the proposed smart traction power grid  was verified on the road-network-vehicle integrated simulation platform. Finally, the key issues that need to be further studied and resolved in engineering practice were provided.

Alexander Felfernig, Stefan Reiterer, Martin Stettinger et al.

The knowledge engineering bottleneck is still a major challenge in configurator projects. In this paper we show how recommender systems can support knowledge base development and maintenance processes. We discuss a couple of scenarios for the application of recommender systems in knowledge engineering and report the results of empirical studies which show the importance of user-centered configuration knowledge organization.

Annika Junker, Julia Timmermann, Ansgar Trächtler

Within this work, we investigate how data-driven numerical approximation methods of the Koopman operator can be used in practical control engineering applications. We refer to the method Extended Dynamic Mode Decomposition (EDMD), which approximates a nonlinear dynamical system as a linear model. This makes the method ideal for control engineering applications, because a linear system description is often assumed for this purpose. Using academic examples, we simulatively analyze the prediction performance of the learned EDMD models and show how relevant system properties like stability, controllability, and observability are reflected by the EDMD model, which is a critical requirement for a successful control design process. Subsequently, we present our experimental results on a mechatronic test bench and evaluate the applicability to the control engineering design process. As a result, the investigated methods are suitable as a low-effort alternative for the design steps of model building and adaptation in the classical model-based controller design method.

Günther Waxenegger-Wilfing, Kai Dresia, Jan Deeken et al.

The last years have witnessed an enormous interest in the use of artificial intelligence methods, especially machine learning algorithms. This also has a major impact on aerospace engineering in general, and the design and operation of liquid rocket engines in particular, and research in this area is growing rapidly. The paper describes current machine learning applications at the DLR Institute of Space Propulsion. Not only applications in the field of modeling are presented, but also convincing results that prove the capabilities of machine learning methods for control and condition monitoring are described in detail. Furthermore, the advantages and disadvantages of the presented methods as well as current and future research directions are discussed.