Hasil untuk "Railroad engineering and operation"

Leszek Czechowski

Abstract: Consideration was given to creating an atmosphere on Mars that would enable people to stay on the surface of Mars without spacesuits. The source of matter for this atmosphere are bodies brought from the outer zones of the Solar System. The Kuiper belt is the best source of these bodies. The energy needed to bring them was estimated. Depending on the variant, this energy ranges from 21% to 800% of the energy currently consumed by humanity annually. Keywords: Terraforming; Mars; Kuiper Belt; Armstrong limit; Gravity assist

Junting Lin, Maolin Li, Xiaohui Qiu

Abstract This paper presents an improved virtual coupling train set (VCTS) operation control framework to deal with the lack of optimization of speed curves in the traditional techniques. The framework takes into account the temporary speed limit on the railway line and the communication delay between trains, and it uses a VCTS consisting of three trains as an experimental object. It creates the virtual coupling train tracking and control process by improving the driving strategy of the leader train and using the leader–follower model. The follower train uses the improved speed curve of the leader train as its speed reference curve through knowledge migration, and this completes the multi-objective optimization of the driving strategy for the VCTS. The experimental results confirm that the deep reinforcement learning algorithm effectively achieves the optimization goal of the train driving strategy. They also reveal that the intrinsic curiosity module prioritized experience replay dueling double deep Q-network (ICM-PER-D3QN) algorithm outperforms the deep Q-network (DQN) algorithm in optimizing the driving strategy of the leader train. The ICM-PER-D3QN algorithm enhances the leader train driving strategy by an average of 57% when compared to the DQN algorithm. Furthermore, the particle swarm optimization (PSO)-based model predictive control (MPC) algorithm has also demonstrated tracking accuracy and further improved safety during VCTS operation, with an average increase of 37.7% in tracking accuracy compared to the traditional MPC algorithm.

Rui Yang, Michael Fu, Chakkrit Tantithamthavorn et al.

Retrieval-augmented generation (RAG)-based applications are gaining prominence due to their ability to leverage large language models (LLMs). These systems excel at combining retrieval mechanisms with generative capabilities, resulting in more accurate, contextually relevant responses that enhance user experience. In particular, Transurban, a road operation company, is replacing its rule-based virtual assistant (VA) with a RAG-based VA (RAGVA) to offer more flexible customer interactions and support a wider range of scenarios. In this paper, drawing from the experience at Transurban, we present a comprehensive step-by-step guide for building a conversational application and how to engineer a RAGVA. These guides aim to serve as references for future researchers and practitioners. While the engineering processes for traditional software applications are well-established, the development and evaluation of RAG-based applications are still in their early stages, with numerous emerging challenges remaining uncharted. To address this gap, we conduct a focus group study with Transurban practitioners regarding developing and evaluating their RAGVA. We identified eight challenges encountered by the engineering team and proposed eight future directions that should be explored to advance the development of RAG-based applications. This study contributes to the foundational understanding of a RAG-based conversational application and the emerging AI software engineering challenges it presents.

Abhik Roychoudhury, Corina Pasareanu, Michael Pradel et al.

Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software engineers requires a level of trust equal to or even greater than the trust established by human-driven software engineering practices. The recent trend toward LLM agents offers a path toward integrating the power of LLMs to create new code with the power of analysis tools to increase trust in the code. This opinion piece comments on whether LLM agents could dominate software engineering workflows in the future and whether the focus of programming will shift from programming at scale to programming with trust.

Xiangping Chen, Xing Hu, Yuan Huang et al.

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many papers have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However, although several surveys have provided overall pictures of the application of deep learning techniques in software engineering, they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this paper, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out the through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically.

Lola Burgueño, Davide Di Ruscio, Houari Sahraoui et al.

Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made in Artificial Intelligence (AI), questions arise about the future of MDE, such as how existing MDE techniques and technologies can be improved or how other activities that currently lack dedicated support can also be automated. However, at the same time, it has to be revisited where and how models should be used to keep the engineers in the loop for creating, operating, and maintaining complex systems. To trigger dedicated research on these open points, we discuss the history of automation in MDE and present perspectives on how automation in MDE can be further improved and which obstacles have to be overcome in both the medium and long-term.

Xin-She Yang

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed.

Aurora Ramírez, José Raúl Romero, Carlos García-Martínez et al.

Although metaheuristics have been widely recognized as efficient techniques to solve real-world optimization problems, implementing them from scratch remains difficult for domain-specific experts without programming skills. In this scenario, metaheuristic optimization frameworks are a practical alternative as they provide a variety of algorithms composed of customized elements, as well as experimental support. Recently, many engineering problems require to optimize multiple or even many objectives, increasing the interest in appropriate metaheuristic algorithms and frameworks that might integrate new specific requirements while maintaining the generality and reusability principles they were conceived for. Based on this idea, this paper introduces JCLEC-MO, a Java framework for both multi- and many-objective optimization that enables engineers to apply, or adapt, a great number of multi-objective algorithms with little coding effort. A case study is developed and explained to show how JCLEC-MO can be used to address many-objective engineering problems, often requiring the inclusion of domain-specific elements, and to analyze experimental outcomes by means of conveniently connected R utilities.

LIAO Linjie, FAN Yi, MEI Xiaoyang et al.

Traditional packaging structures of power semiconductor device use aluminum (Al) wire for bonding. This leads to high parasitic inductance and reliability issues, limiting the development of silicon carbide (SiC) power devices. Researchers have proposed a new copper clip interconnection process that enables double-sided heat dissipation and improves the power density of the devices. However, current research mainly focuses on its thermal performance and reliability, lacking exploration of structural design optimization. Further research is necessary to optimize the structure design of multi-chip copper clip interconnections. This study investigated the influence of critical structural parameters of the copper clip power devices on chip stress concentration through simulations. The results indicate that the copper clip thickness has the most significant impact on chip stress concentration, while the copper clip span has the least influence. Optimal structural parameter which was compared with the smallest solder layer stress were used to establish a copper clip device model and the corresponding wire-bonded module. The findings reveal that, under power cycling, the copper clip device shows a more than 10 times improvement in the fatigue life of both the copper clip and solder layer compared with the wire-bonded module. And the unloading groove significantly helps improve the fatigue life of copper clip devices.

CHEN Boqing, ZENG Yuanchen, SONG Dongli et al.

Wheels are the key components of EMU and degradation occurs on wheels after long-term service, impeding the operation safety and stability of EMU. In addition, the wheels of the entire train of EMU are operated and maintained in groups, so the degradation of each wheel has overall synchronization and individual differences, and adjacent wheels affect each other in the operation and maintenance (O&M) processes. Wheel degradation is characterized by diverse influencing factors, mutual influence of parameters and high randomness, so a numerical simulation method was proposed for the degradation of whole-train wheels of EMU in full-process O&M. A probabilistic model that incorporated the relevant factors, degradation laws and uncertainties is used to simulate the key factors in the operation and maintenance process, such as wheel wear, defects and reprofiling, and then the numerical simulation of the wheel degradation process was realized through closed-loop iteration. Then, a numerical simulation method of wheel degradation based on Monte Carlo simulation is proposed to simulate the degradation process of whole-train wheels of EMU under the influence in random links. Finally, based on the operation and maintenance data of EMU in China, modeling and simulation examples were given. The results show that the method can effectively predict the average level and uncertainties of degradation of whole-train wheels of EMU, enabling forecast of the remaining service life of wheels. The method can also be used to optimize of the overhaul limits of wheels based on the wheel operation and maintenance cost evaluation, so as to reduce the cost in the whole process of wheel operation and maintenance and improve the economy of EMU maintenance.

Sajid Ibrahim Hashmi, Jouni Markkula

One of the objectives of software engineering education is to make students to learn essential teamwork skills. This is done by having the students work in groups for course assignments. Student team composition plays a vital role in this, as it significantly affects learning outcomes, what is learned, and how. The study presented in this paper aims to better understand the student team composition in software engineering education and investigate the factors affecting it in the international software engineering education context. Those factors should be taken into consideration by software engineering teachers when they design group work assignments in their courses. In this paper, the initial findings of the ongoing Action research study are presented. The results give some identified principles that should be considered when designing student team composition in software engineering courses.

Eriks Klotins, Michael Unterkalmsteiner, Tony Gorschek

Software start-up failures are often explained with poor business model, market issues, insufficient funding, or simply a bad product idea. However, inadequacies in software product engineering are relatively little explored and could be a significant contributing factor to high start-up failure rate. In this paper we present analysis of 88 start-up experience reports. The analysis is presented in a form of three anti-patterns illustrating common symptoms, actual causes, and potential countermeasures of engineering inadequacies. The three anti-patterns are: product uncertainty comprising of issues in requirements engineering, poor product quality comprising of inadequacies in product quality, and team breakup comprising of team issues. The anti-patterns show that challenges and failure scenarios that appear to be business or market-related can actually originate from inadequacies in product engineering.

Paloma Guenes, Rafael Tomaz, Marcos Kalinowski et al.

The Impostor Phenomenon (IP) is widely discussed in Science, Technology, Engineering, and Mathematics (STEM) and has been evaluated in Computer Science students. However, formal research on IP in software engineers has yet to be conducted, although its impacts may lead to mental disorders such as depression and burnout. This study describes a survey that investigates the extent of impostor feelings in software engineers, considering aspects such as gender, race/ethnicity, and roles. Furthermore, we investigate the influence of IP on their perceived productivity. The survey instrument was designed using a theory-driven approach and included demographic questions, an internationally validated IP scale, and questions for measuring perceived productivity based on the SPACE framework constructs. The survey was sent to companies operating in various business sectors. Data analysis used bootstrapping with resampling to calculate confidence intervals and Mann-Whitney statistical significance testing for assessing the hypotheses. We received responses from 624 software engineers from 26 countries. The bootstrapping results reveal that a proportion of 52.7% of software engineers experience frequent to intense levels of IP and that women suffer at a significantly higher proportion (60.6%) than men (48.8%). Regarding race/ethnicity, we observed more frequent impostor feelings in Asian (67.9%) and Black (65.1%) than in White (50.0%) software engineers. We also observed that the presence of IP is less common among individuals who are married and have children. Moreover, the prevalence of IP showed a statistically significant negative effect on the perceived productivity for all SPACE framework constructs. The evidence relating IP to software engineers provides a starting point to help organizations find ways to raise awareness of the problem and improve the emotional skills of software professionals.

Samah Syed, Angel Deborah S

In software development, code comments play a crucial role in enhancing code comprehension and collaboration. This research paper addresses the challenge of objectively classifying code comments as "Useful" or "Not Useful." We propose a novel solution that harnesses contextualized embeddings, particularly BERT, to automate this classification process. We address this task by incorporating generated code and comment pairs. The initial dataset comprised 9048 pairs of code and comments written in C, labeled as either Useful or Not Useful. To augment this dataset, we sourced an additional 739 lines of code-comment pairs and generated labels using a Large Language Model Architecture, specifically BERT. The primary objective was to build classification models that can effectively differentiate between useful and not useful code comments. Various machine learning algorithms were employed, including Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gradient Boosting, Random Forest, and a Neural Network. Each algorithm was evaluated using precision, recall, and F1-score metrics, both with the original seed dataset and the augmented dataset. This study showcases the potential of generative AI for enhancing binary code comment quality classification models, providing valuable insights for software developers and researchers in the field of natural language processing and software engineering.

Eriks Klotins, Tony Gorschek

Software is becoming a critical component of most products and organizational functions. The ability to continuously improve software determines how well the organization can respond to market opportunities. Continuous software engineering promises numerous advantages over sprint-based or plan-driven development. However, implementing a continuous software engineering pipeline in an existing organization is challenging. In this invited position paper, we discuss the adoption challenges and argue for a more systematic methodology to drive the adoption of continuous engineering. Our discussion is based on ongoing work with several industrial partners as well as experience reported in both state-of-practice and state-of-the-art. We conclude that the adoption of continuous software engineering primarily requires analysis of the organization, its goals, and constraints. One size does not fit all purposes, meaning that many of the principles behind continuous engineering are relevant for most organizations, but the level of realization and the benefits may still vary. The main hindrances to continuous flow of software arise from sub-optimal organizational structures and the lack of alignment. Once those are removed, the organization can implement automation to further improve the software delivery.

Zeinab Abou Khalil, Stefano Zacchiroli

We introduce the General Index of Software Engineering Papers, a dataset of fulltext-indexed papers from the most prominent scientific venues in the field of Software Engineering. The dataset includes both complete bibliographic information and indexed ngrams (sequence of contiguous words after removal of stopwords and non-words, for a total of 577 276 382 unique n-grams in this release) with length 1 to 5 for 44 581 papers retrieved from 34 venues over the 1971-2020 period.The dataset serves use cases in the field of meta-research, allowing to introspect the output of software engineering research even when access to papers or scholarly search engines is not possible (e.g., due to contractual reasons). The dataset also contributes to making such analyses reproducible and independently verifiable, as opposed to what happens when they are conducted using 3rd-party and non-open scholarly indexing services.The dataset is available as a portable Postgres database dump and released as open data.

E. A. Zubkova, T. A. Karpeeva

The article reflects the most important areas of work of scientists and employees of the scientific center “Express” and their contribution to the formation and development of information technology since the founding in 1959 of the Department of computer science of the All-Union Scientific Research Institute of Railway Transport and up to the present.Theoretical foundations of the application of transport cybernetics in the national economy of the country were created by Corresponding Member of the Academy of Sciences of the USSR A. P. Petrov, who was one of the first to use mathematical methods and computing technology in solving transport problems and headed the development in this direction at the institute. The practical work on the creation of the first domestic automated system for selling tickets for trains, called “Express”, was headed by B. E. Marchuk.Since then, the “Express” system has transformed from a local ticketing system at the Kievsky railway terminal in Moscow into a powerful multifunctional international passenger traffic management system that interacts with the ticket reservation systems of European countries.Theoretical and practical developments carried out by the scientific center have resulted in the introduction of new information technologies in the Russian Railways holding and in the railway administrations of the member states of the Commonwealth.At present, the main activities of the staff of the scientific center are the creation of automated control system “Express” of a new generation (ACS “Express” NP) and the system of international integration of passenger transportation Express International.

S. K. Zhao, Zi-Yong Ge, Zhongcheng Xiang et al.

Operator spreading, often characterized by out-of-time-order correlators (OTOCs), is one of the central concepts in quantum many-body physics. However, measuring OTOCs is experimentally challenging due to the requirement of reversing the time evolution of systems. Here we apply Floquet engineering to investigate operator spreading in a superconducting 10-qubit chain. Floquet engineering provides an effective way to tune the coupling strength between nearby qubits, which is used to demonstrate quantum walks with tunable couplings, reversed time evolution, and the measurement of OTOCs. A clear light-cone-like operator propagation is observed in the system with multiple excitations, and has a nearly equal velocity as the single-particle quantum walk. For the butterfly operator that is nonlocal (local) under the Jordan-Wigner transformation, the OTOCs show distinct behaviors with (without) a signature of information scrambling in the near integrable system.

Mamdouh Alenezi

Digital transformation is a hot topic in the current global environment as a large number of organizations have been working to adopt digital solutions. Software engineering has also emerged to be a more important role as a large number of systems, either traditional or smart, are dependent on the software that collects, store, and process data. The role of software engineers has also become crucial in digital transformation. In this regard, this paper aims to examine the trends of software engineering and the role of software engineers in digital transformation. In addition to this, this paper also examines the importance of secure software development in digital transformation. It can be concluded that software engineering is an integral part of digital transformation as all digital systems make use of software to perform their functions efficiently. Software act as a bridge between digital systems and humans to use the systems interactively and efficiently.

Tao GUO, Ping SHEN, Lide WANG et al.

A highly reliable ladder redundant structure networking scheme applied to train communication network was constructed, and the fault protection and redundant switching mechanism of the network were analyzed. For complex TCN such as ladder networks, a simplified dynamic fault tree analysis(DFTA) method based on K-terminal connectivity detection was proposed, and a binary decision diagram(BDD) was used to simplify the fault tree calculation process. The algorithm proposed comprehensively considered the system structure and dynamic repair of devices in the network and the influence of the topology on the network reliability, which simplified the modeling and calculation process of the fault tree model. The paper analyzed the proposed algorithm for the reliability of the trapezoidal redundant structure for specific examples. The results show that the ladder structure has an increase in the mean time between failures by 1 513.32 h compared to the use of PRP and 5 034.61 h longer than the use of HSR under the same scenario, which significantly improved the reliability. The networking scheme constructed in this paper and the proposed reliability modeling method provided new ideas for the structural design and reliability modeling analysis of the highly reliable train communication network.