Hasil untuk "Railroad engineering and operation"

Karina Kohl, Luigi Carro

Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.

Cristiam Alejandro Rodríguez Sis, Maira María Pérez Villar, Luis Ernesto Morera Hernández et al.

In the present research, a characterization of the effluent wastewater from the primary treatment of a detailing shop (belonging to a railroad construction company at the center of Cuba) was carried out. The effluent does not comply with the Average Maximum Permissible Limits (AMPL) established by the Cuban Standard (NC 27:2012)1 and as a solution a 12 m2 subsurface wetland was designed and planted with Cyperus Alternifolius L. The removal efficiencies were above 85% and after three years were greater than 90%, in coincidence with a massive increase in vegetation. In addition, a kinetic study was carried out, obtaining a better adjustment to the saturation or Monod kinetic model. The kinetic and saturation constants determined for organic matter removal (in a three-day period of operation) with variations in the influent COD concentration will allow the subsequent design of other subsurface wetlands under similar climatic and operating conditions.

Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy et al.

Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.

Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon et al.

In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs, proactively initiating and nurturing collaborations, and engaging within each other's professional environments. They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another. Additionally, the guidelines highlight the necessity of vigilance in monitoring collaboration dynamics, securing institutional support, and defining clear, shared objectives. By adhering to these principles, RSEs and SERs can build synergistic relationships that enhance the quality and impact of research outcomes.

Shardul Chiplunkar, Clément Pit-Claudel

Railroad diagrams (also called "syntax diagrams") are a common, intuitive visualization of grammars, but limited tooling and a lack of formal attention to their layout mostly confines them to hand-drawn documentation. We present the first formal treatment of railroad diagram layout along with a principled, practical implementation. We characterize the problem as compiling a *diagram language* (specifying conceptual components and how they connect and compose) to a *layout language* (specifying basic graphical shapes and their sizes and positions). We then implement a compiler that performs *line wrapping* to meet a target width, as well as vertical *alignment* and horizontal *justification* per user-specified policies. We frame line wrapping as an optimization problem, where we describe principled dimensions of optimality and implement corresponding heuristics. For front-end evaluation, we show that our diagram language is well-suited for common applications by describing how regular expressions and Backus-Naur form can be compiled to it. For back-end evaluation, we argue that our compiler is practical by comparing its output to diagrams laid out by hand and by other tools.

Oz Levy, Ilya Dikman, Natan Levy et al.

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

Lekshmi Murali Rani

The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.

Xiyang Liu, Kaibin Yan, Lie Guo et al.

As the mileage of electrified railroads continues to expand, insulators, as a key component of the power transmission system, are crucial to the cleaning and maintenance of insulators to ensure the safety of railroad power supply. However, the dirt deposit-ed in daily use leads to the frequent occurrence of dirt flash accidents, and the traditional manual cleaning method can no longer meet the safety requirements of modern railroad operation due to its low efficiency, high operational risk and difficulty in adapting to the needs of large-scale maintenance. In view of the above problems, this paper proposes a servo control system design method based on current feedback, and designs an insulator wiping end-effector based on current feedback control. Firstly, the mechanical structure and control module design are proposed, and the three-dimensional parametric model is constructed through SolidWorks to optimize the actuator kinematic characteristics. Secondly, a high-precision servo control system is developed based on the STM32F407 master control platform, which innovatively integrates the PID regulation technology, the high-sensitivity current acquisition module and the sliding average filtering algorithm to realize the real-time dynamic and precise control of the contact pressure between the brush and insulator. Then, the model is constructed, and the actuator prototype is completed through electromechanical coupling, and simulation experiments are conducted to verify the feasibility of the project. Finally, the operation verification is carried out by building experimental platform to simulate the railway environment, and the results show that the actuator can intelligently adapt to a variety of installation postures, such as horizontal, vertical and tilted, and the accuracy of contact pressure control reaches ±0.2kg, and the efficiency of a single cleaning is improved compared with the traditional manual method. This study effectively solves the technical problem of unmanned cleaning and maintenance of railroad insulators, and provides a generalizable technical solution for the research and development of intelligent unmanned operation and maintenance equipment for contact networks.

Dandan Dang, Li Gong, Chunling Jin et al.

CHEN Zuhang, LIN Wenbiao, SUN Haoran et al.

To address the challenges associated with early warning of voltage anomalies in the traction battery systems of new-energy locomotives, a multi-parameter fusion-based approach is proposed to assess voltage consistency. By systematically analyzing the voltage characteristics of battery packs, we established an evaluation framework that incorporates key metrics—mean voltage, voltage range, root mean square deviation of voltage, coefficient of voltage variation, and extreme voltage changes—and developed a voltage consistency evaluation coefficient model. The model enables quantitative evaluation of voltage consistency at three hierarchical levels: battery system, battery branch, and battery pack. Cross-comparison of the coefficients across different battery cells allows rapid localization of anomalous cells. The proposed method for early warning of voltage anomalies provides strong assurance for the safe and stable operation of new-energy locomotives, offering significant theoretical value and practical engineering guidance.

Sebastian Cisowski

Abstract: The development of modern driver assistance systems, such as Adaptive Cruise Control (ACC) and its enhanced version ACC+, represents a significant step towards improving road safety. This article analyzes the operation of these systems in the context of the causes and effects of traffic accidents. Based on the reconstruction of a specific traffic incident, a detailed assessment of the impact of ACC and ACC+ on traffic safety was conducted. The study is based on the analysis of measurable technical parameters that change under varying road conditions, both with the systems activated and deactivated. The paper also discusses examples of preventive strategies for similar incidents, utilizing new technologies applied in modern vehicles. Particular attention is given to differences in vehicle behavior under various operational scenarios of ACC and ACC+ systems, with a focus on their impact on the safety of road users. This article contributes to the development of knowledge on minimizing accident risks through advanced driver assistance systems. Keywords: ACC; ACC+; Safety; Traffic

O. A. Qowiy, W. Aspar, Herry Susanto et al.

In the transportation network, railway bridges are crucial for the transfer of both passengers and commodities. Railway bridges require continuous monitoring to observe their performance. A structural health monitoring system is one method for assessing the viability of a railway bridge structure. The functioning of railroad bridge structures has been extensively observed using wireless technology. This research aims to implement smart wireless sensors for monitoring the structural health of the railway bridge online in real-time during operation. Many sensor kinds were installed on the railway bridge, including strain gauges, accelerometers, linear variable displacement transducers, and proximity sensors. Geometric modeling and numerical simulation were performed to find critical frame locations on the railway bridge where the instrumentation sensors would be placed. In this study, MONITA® is employed for data acquisition modules. The MONITA® system consists of a combination of hardware and software that functions to retrieve, send, store, and process data. This paper describes the result of the establishment of this method to comprehend the performance of the steel railway bridge structure in real-time via the human-machine interface display dashboard. As a result, the monitoring system can appropriately be used to assess a structural railway bridge in real-time. This study may be helpful to practicing engineers and researchers in future studies of steel railway bridge evaluation. This could be a useful reference for future studies in implementing such systems as the railway bridge early warning system technique in detecting bridge damage.

Al Amin, Deo Chimba, Kamrul Hasan et al.

Crashes and delays at Railroad Highway Grade Crossings (RHGC), where highways and railroads intersect, pose significant safety concerns for the U.S. Federal Railroad Administration (FRA). Despite the critical importance of addressing accidents and traffic delays at highway-railroad intersections, there is a notable dearth of research on practical solutions for managing these issues. In response to this gap in the literature, our study introduces an intelligent system that leverages machine learning and computer vision techniques to enhance safety at Railroad Highway Grade crossings (RHGC). This research proposed a Non-Maximum Suppression (NMS)- based ensemble model that integrates a variety of YOLO variants, specifically YOLOv5S, YOLOv5M, and YOLOv5L, for grade-crossing object detection, utilizes segmentation techniques from the UNet architecture for detecting approaching rail at a grade crossing. Both methods are implemented on a Raspberry Pi. Moreover, the strategy employs high-definition cameras installed at the RHGC. This framework enables the system to monitor objects within the Region of Interest (ROI) at crossings, detect the approach of trains, and clear the crossing area before a train arrives. Regarding accuracy, precision, recall, and Intersection over Union (IoU), the proposed state-of-the-art NMS-based object detection ensemble model achieved 96% precision. In addition, the UNet segmentation model obtained a 98% IoU value. This automated railroad grade crossing system powered by artificial intelligence represents a promising solution for enhancing safety at highway-railroad intersections.

Adrian Bajraktari, Michelle Binder, Andreas Vogelsang

Modern science is relying on software more than ever. The behavior and outcomes of this software shape the scientific and public discourse on important topics like climate change, economic growth, or the spread of infections. Most researchers creating software for scientific purposes are not trained in Software Engineering. As a consequence, research software is often developed ad hoc without following stringent processes. With this paper, we want to characterize research software as a new application domain that needs attention from the Requirements Engineering community. We conducted an exploratory study based on 8 interviews with 12 researchers who develop software. We describe how researchers elicit, document, and analyze requirements for research software and what processes they follow. From this, we derive specific challenges and describe a vision of Requirements Engineering for research software.

Yifeng Yang, Thelma Domingo Palaoag

Railroad transportation is an important infrastructure for public travel and cargo transportation. With the rapid development of railroad construction, the mileage of operation continues to increase and the road network continues to improve, and the railroad covers a wider range of terrain, making the environment for train travel more complex. The behavior of individuals or groups entering the railroad track without permission or authorization may cause serious harm to the life safety of personnel and the normal operation of railroad traffic. Therefore, this paper aimed to propose an improved YOLOV5-based track personnel intrusion detection algorithm, which improves the recall rate by 14% and reduces the loss rate by introducing the CBAM attention mechanism into the C3 layer of the three pyramid strata of YOLO, achieving an average precision of 98%. The results of experimental simulation using the improved model on the acquired image data to be detected for unauthorized personnel intrusion into the track show that the machine vision-based railroad track personnel intrusion detection algorithm in this paper takes full account of the characteristics of the railroad scenario, and the processing has a high detection precision. The finding of the study can make contribution to the Railway Bureau to effectively detect the risk of railroad safety and reduce the probability of accidents.

Dongwoo Jin, Soyun Moon, Seungun Chae et al.

Underground railroad stations face evacuation challenges owing to the rapid spread of smoke and extended escape routes during fires. Evacuation in deeply underground domestic railroad stations relies solely on stairways, with no legal standards for additional evacuation methods. In contrast, international standards such as NFPA 130 and Japan’s Fire Protection Engineering Association (JAFPE) recommend using evacuation elevators as a supplementary means of evacuation. This study analyzed smoke behavior in deep underground railroad stations through fire simulations at the Soongsil University and Guryong station. It also examined additional evacuation measures to enhance evacuation safety through evacuation simulations. The results show that during a fire, smoke spread follows the same path as the passenger evacuation routes because of the chimney effect. Even with smoke control and ventilation systems in operation, smoke follows the same evacuation routes, such as stairways or escalators. Therefore, the study concluded that evacuation elevators should be installed as an additional means to improve evacuation efficiency, and placed in smoke-protected evacuation routes. The study also found that combining evacuation stairways and elevators significantly enhances the evacuation efficiency.

Changhai Tian, Shoushuai Zhang

Purpose – The design goal for the tracking interval of high-speed railway trains in China is 3 min, but it is difficult to achieve, and it is widely believed that it is mainly limited by the tracking interval of train arrivals. If the train arrival tracking interval can be compressed, it will be beneficial for China's high-speed railway to achieve a 3-min train tracking interval. The goal of this article is to study how to compress the train arrival tracking interval. Design/methodology/approach – By simulating the process of dense train groups arriving at the station and stopping, the headway between train arrivals at the station was calculated, and the pattern of train arrival headway was obtained, changing the traditional understanding that the train arrival headway is considered the main factor limiting the headway of trains. Findings – When the running speed of trains is high, the headway between trains is short, the length of the station approach throat area is considerable and frequent train arrivals at the station, the arrival headway for the first group or several groups of trains will exceed the headway, but the subsequent sets of trains will have a headway equal to the arrival headway. This convergence characteristic is obtained by appropriately increasing the running time. Originality/value – According to this pattern, there is no need to overly emphasize the impact of train arrival headway on the headway. This plays an important role in compressing train headway and improving high-speed railway capacity.

Fityanul Akhyar, Nur Ibrahim, Koredianto Usman et al.

During the operation of high-intensity trains, various types of defects often arise, resulting in minor to moderate damage to the rail surface. Surface anomalies on railroad tracks can lead to increased speeds, resulting in elevated noise levels and a higher risk of train accidents. To enhance quality standards, manual inspections by field workers are necessary. However, these inspections require significant manpower, suffer from accuracy issues, and incur substantial costs. To streamline the inspection process, we analyzed a deep learning-based surface flaw detection system that employed three variations of the You Only Look Once (YOLO) algorithm: YOLOv6, YOLOv7, and YOLOv8. The aim was to improve the efficiency of the sorting stage. Furthermore, our experiments focused on converting pixel labels into rectangular or bounding box labels using the RSDDs dataset, which comprises two primary categories: high-speed rail (type 1) and heavy rail (type 2). Given the challenging nature of this dataset, the defect detection system achieved accuracies of 92.7% for YOLOv6-L6, 95.6% for YOLOv7-D6, and 99.5% for YOLOv8-S within the type 1 category. In the type 2 category, the results were 88.03% for YOLOv6-S6, 88.5% for YOLOv7W6, and 91.3% for YOLOv8M. These comprehensive experimental findings demonstrate that the YOLOv8 variant holds great potential in terms of mean average precision (mAP) accuracy for rail surface inspection systems utilizing rectangular-shaped labels.

Jan Heine, Herbert Palm

The increasing pressure within VUCA (volatility, uncertainty, complexity and ambiguity) driven environments causes traditional, plan-driven Systems Engineering approaches to no longer suffice. Agility is then changing from a "nice-to-have" to a "must-have" capability for successful system developing organisations. The current state of the art, however, does not provide clear answers on how to map this need in terms of processes, methods, tools and competencies (PMTC) and how to successfully manage the transition within established industries. In this paper, we propose an agile Systems Engineering (SE) Framework for the automotive industry to meet the new agility demand. In addition to the methodological background, we present results of a pilot project in the chassis development department of a German automotive manufacturer and demonstrate the effectiveness of the newly proposed framework. By adopting the described agile SE Framework, companies can foster innovation and collaboration based on a learning, continuous improvement and self-reinforcing base.

Xicheng Wu, Yingzhi Wang, Shuiming Wang

: In recent years, the transportation capacity of Shuohuang Railway, an important coal transportation corridor in China, has become saturated. To exploit potentialities and raise efficiency, transportation organization optimization is the key way in the short term. Based on elaborating the present situation of traffic volume on the Shuozhou-Huanghua railway, the traffic volume of 2030 is predicted by the time series method, which is found that under the condition of existing equipment and transportation organization, the railroad capacity is not able to meet the transportation demand. This paper analyzes the technical conditions under the traffic volume target of 450 million tons and proposes the phased optimization steps of transportation organization on the Shuozhou-Huanghua railway from two aspects of station operation efficiency and train transportation organization based on a new type of automatic parking device. A series of optimization measures for capacity expansion and efficiency improvement are raised, such as dwell time compression of