Procedia Computer Science最新文献

{"title":"A neural network approach for collaborative cells: an innovative online rescheduling strategy for maximizing productivity","authors":"Irene Granata ,&nbsp;Matthias Bues ,&nbsp;Martina Calzavara ,&nbsp;Maurizio Faccio ,&nbsp;Benjamin Wingert","doi":"10.1016/j.procs.2025.01.103","DOIUrl":"10.1016/j.procs.2025.01.103","url":null,"abstract":"<div><div>Transitioning from Industry 4.0 to Industry 5.0 signifies a significant change in how technology integrates with workplace dynamics. While Industry 4.0 focused on streamlining production through automation, Industry 5.0 centers on human-centric approaches. This entails designing work environments that prioritize human comfort and efficiency by incorporating technology that complements human capabilities. Collaborative robots, known as cobots, play a pivotal role in this shift, aiding humans in tasks while fostering increased human involvement. However, maximizing the benefits of cobots necessitates workspace designs that optimize both human and robotic resources’ needs and preferences. A promising strategy involves implementing a dynamic task allocation system. This approach employs a neural network to adaptively reallocate tasks to prevent any loss in performance. Such advancements represent a significant stride towards establishing production settings that prioritize the effectiveness of human workers.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 415-424"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Company perspectives of generative artificial intelligence in industrial work","authors":"Susanna Aromaa ,&nbsp;Päivi Heikkilä ,&nbsp;Marko Jurvansuu ,&nbsp;Selen Pehlivan ,&nbsp;Teijo Väärä ,&nbsp;Marko Jurmu","doi":"10.1016/j.procs.2025.01.085","DOIUrl":"10.1016/j.procs.2025.01.085","url":null,"abstract":"<div><div>The use of artificial intelligence (AI) technologies in the manufacturing industry is rapidly increasing. During this transformation, it can be difficult to understand how AI will change the way work is done. This study explores how generative AI could change manufacturing work. Data collection was conducted using interviews and a questionnaire with seven representatives from three industrial companies. They identified several application areas for GenAI in the industrial work context, such as design, planning, training, problem solving, coding and data management. They also expressed positive attitudes but raised concerns about trust, safety, acceptability and interoperability. Changes in work were identified as being more related to cognitive aspects such as changing the way of thinking and altering the interaction with people and machines. Therefore, human-AI design efforts should focus especially on cognitive ergonomics. Findings from this study can be used in the manufacturing industry when adopting AI, as well as in identifying research topics in the human-AI research community.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 217-226"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparison of Educational Perspectives on VDI 2221 and Axiomatic Design","authors":"Patrick Kröpfl ,&nbsp;Christian Landschützer ,&nbsp;Hannes Hick ,&nbsp;Wajih Haider Awan ,&nbsp;Christopher A. Brown","doi":"10.1016/j.procs.2025.01.073","DOIUrl":"10.1016/j.procs.2025.01.073","url":null,"abstract":"<div><div>Engineering design methods play a crucial role in both academia and industry. These systematic approaches facilitate product and system development, allowing for innovative solutions and refinements. Specifically, this paper will compare two common engineering design methods Axiomatic Design (AD) and VDI 2221 in terms of their application in teaching and their transferability to industry, especially for small and medium-sized enterprises (SMEs). Firstly, a quantitative comparison of the two methods will be conducted. Comparative factors will include scope, accessibility, required prior knowledge, and the availability of tools for each method. Following this, insights from teaching experiences at the Technical University of Graz and Worcester Polytechnic Institute (WPI) will be discussed, focusing on the teachability of the methods. This will provide insights into the effectiveness and suitability of the methods for higher education. The transfer potential of the methods to SMEs will be derived from these. Finally, the findings and improvement potential will be summarized, and possibilities for the knowledge transfer of engineering design methods to SMEs will be formulated.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 94-103"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Chipless RFID Technology to Provide Seamless Data Interoperability for Textile Industry Circularity","authors":"Maximilian Scholz,&nbsp;Omid Fatahi Valilai","doi":"10.1016/j.procs.2025.01.101","DOIUrl":"10.1016/j.procs.2025.01.101","url":null,"abstract":"<div><div>The textile industry faces tremendous challenges when it comes to waste management and recycling. The current methods for textile companies and drop-off centres for sorting the textiles for recycling is largely through manual labour, which is inefficient and involves high costs. The bottleneck due to slow process for visual inspection creates bottlenecks for effective sorting. One idea to solve this problem is to use an embedded data mechanism in textile tags via radio frequency identification (RFID) chips. Considering the requirements of recycling processes, there is an essential need for RFID technologies which are compatible with recyclability of textile processes. Therefore, the need and demand for a sustainable solution for traceability and recycling via chipless RFID technologies is highly motivated. Moreover, the technology should be economically viable for industries for adoption. This study explores a new technological concept that offers a solution for the current problem of creating a circular economy in the textile industry with traceability of data. So, the study focuses on analysing how chipless RFID technology may be integrated into textiles with 3D printing technology. The research investigates 3D printing technology for providing the ability to create a fast, inexpensive, and detailed chipless RFID labelling solution for textile materials. Finally, the paper investigates the consumer populations readiness to adopt the technology by identifying pain points and outlining the integration of this technology into the textile industry.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 393-402"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Material Fatigue Testing Strategies regarding Failure-Free Load Level of Steel Specimens using Bootstrapping and Statistical Models","authors":"Nikolaus Haselgruber ,&nbsp;Gerhard Oertelt ,&nbsp;Kristopher Boss","doi":"10.1016/j.procs.2025.01.095","DOIUrl":"10.1016/j.procs.2025.01.095","url":null,"abstract":"<div><div>The analysis of material fatigue data is an important step in the development of complex technical products to achieve a design which reliably withstands field load but avoids over-engineered and further unnecessary weight, energy consumption, and consequently, life cycle costs. The application of statistical methods helps to consider both, the variability of real-world load situations and the variability of material load capacity. However, to provide effective and accurate results, not only analysis methods but also data generation techniques should be selected with care. In this paper, we compare several material fatigue evaluation strategies, all consisting of a data generation/test part and an analysis part. E.g., stair-case, load-step and pearl-string as test procedures and Dixon-Mood analysis, lifetime-stress regression or the random fatigue limit model as analysis methods are investigated. The sensitivity on parameters which have to be set and the accuracy regarding load capacity as well as the required testing effort are compared. Load-step provides the most accurate estimation of the failure-free load level but is the most expensive method. Pearl-string and DoE provide similar results with much less effort and moderately higher uncertainty compared to load-step.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 323-335"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards AI-enhanced process planning: assessing machine tool capability based on part design","authors":"Sepideh Abolghasem ,&nbsp;Matthew Youssef ,&nbsp;Faruk Abedrabbo ,&nbsp;Amman Pandde","doi":"10.1016/j.procs.2025.01.122","DOIUrl":"10.1016/j.procs.2025.01.122","url":null,"abstract":"<div><div>The emergence of the fourth industrial revolution, or Industry 4.0, necessitates a more automated approach to manufacturing process planning. This process begins with evaluating machine tool capabilities to handle specific part geometries and microstructures. Once a match is established, the focus shifts to developing an efficient method for converting design elements into physical components. This work aims to create and validate a framework that assesses the manufacturability of design features based on the available machinery and materials. Specifically, it involves classifying manufacturing processes, such as turning and milling, for a given part design geometry. To achieve this, feature attributes like rotational symmetry and D2 distribution are calculated for a dataset used to train a decision tree. This model then suggests the appropriate manufacturing process for a given CAD model. The decision tree is validated with a separate dataset, showing reasonable accuracy. Ultimately, the goal is to enhance process planning, ensuring the seamless translation of designs into physical products, with a particular emphasis on geometry, microstructure, and cost.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"253 ","pages":"Pages 603-611"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MASFENON: implementing a multi-agent simulation framework for interconnected networks with distributed programming","authors":"Giorgio Locicero ,&nbsp;Antonio Di Maria ,&nbsp;Salvatore Alaimo ,&nbsp;Alfredo Pulvirenti","doi":"10.1016/j.procs.2025.02.262","DOIUrl":"10.1016/j.procs.2025.02.262","url":null,"abstract":"<div><div>The complexity of networked systems, particularly interconnected networks, necessitates advanced simulation frameworks to accurately emulate real-world dynamics, especially in the context of big data and high-performance computing. Most software used for simulation and temporal inference usually falls short in large data and optimization, since it is generally used in particular contexts, like simulating the dynamics of a specific group of entities, such as cellular and community interactions. We present ”Multi-Agent Adaptive Simulation Framework for Evolution in Networks of Networks” (MASFENON). MASFENON employs a temporal multi-layered approach to simulate and analyze dynamic processes in interconnected networks. The framework leverages parallel programming techniques for matrix and linear algebra operations and distributed and reactive programming for agent and environment communication, all implemented in C++ using the Message Passing Interface (MPI) standard. MASFENON has been validated against several common network models and could simulate the behavior of real systems in the context of epidemic simulations (See [<span><span>4</span></span>]). The framework demonstrates sublinear speedup and scalability with network size. The implementation is open source and available in a regularly updated GitHub repository1. MASFENON's integration of MPI and distributed programming techniques provides a powerful and versatile tool for modeling complex network interactions and dynamics. Its capabilities extend beyond traditional models, offering new insights and applications in network science.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"255 ","pages":"Pages 73-82"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards full AI model lifecycle management on EuroHPC systems, experiences with AIFS for DestinE","authors":"Thomas Geenen ,&nbsp;Even Marius Nordhagen ,&nbsp;Victor Sanchez ,&nbsp;Cathal O'Brien ,&nbsp;Simon Lang ,&nbsp;Mihai Alexe ,&nbsp;Ana Prieto Nemesio ,&nbsp;Gert Mertes ,&nbsp;Rakesh Prithiviraj ,&nbsp;Jesper Dramsch ,&nbsp;Baudouin Raoult ,&nbsp;Florian Pinault ,&nbsp;Helen Theissen ,&nbsp;Sara Hahner ,&nbsp;Mario Santa Cruz ,&nbsp;Matthew Chantry ,&nbsp;Nils Wedi","doi":"10.1016/j.procs.2025.02.264","DOIUrl":"10.1016/j.procs.2025.02.264","url":null,"abstract":"<div><div>On October 13 2023 ECMWF released the first alpha version of its artificial intelligence forecasting system, AIFS, ECMWFs data-driven forecasts model. This first release came just a few months after ECMWF started the development of this new model that highlights the increased efforts in the field of machine learning (ML) that ECMWF has been building over the last few years. This paper describes the use of AIFS on EuroHPC systems in the context of DestinE. The main focus is on performance benchmarks on the different EuroHPC systems available to DestinE but also very much on the deployment and use of the tools to support the model lifecycle management. EuroHPC systems have already proven to be of great value for DestinE and in this paper, we describe how we leverage these systems for artificial intelligence (AI) and ML models in DestinE. We are closely working with EuroHPC and EuroHPC hosting sites through co-design and the optimization of existing solutions to optimize the usage of these systems in every step of the lifecycle management for AI and ML models. The performance benchmarks of our models on several EuroHPC systems showed that the speedup is close to linear up to several thousand GPUs, but that for each EuroHPC system a different optimization strategy must be used to achieve that. For model lifecycle management we found that we can use our in-house developed, domain specific, framework on EuroHPC systems and highlight some specific modifications and future improvements for EuroHPC systems. W e a l s o provide implementation details and share our experiences on how to retrieve and collect provenance data and information from models running on EuroHPC systems using (external to the EuroHPC system deployed) cloud native frameworks. Although we describe solutions in this paper that are designed to support our specific requirements and context, we believe that proposed solutions, developments and implementation details can also bring value beyond the broader NWP community.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"255 ","pages":"Pages 93-102"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Portable test run of ESPResSo on EuroHPC systems via EESSI","authors":"Alan O'Cais ,&nbsp;Kenneth Hoste ,&nbsp;Jean-Noël Grad ,&nbsp;Caspar van Leeuwen ,&nbsp;Lara Peeters ,&nbsp;Satish Kamath ,&nbsp;Thomas Röblitz ,&nbsp;Richard Topouchian ,&nbsp;Bob Dröge ,&nbsp;Pedro Santos Neves ,&nbsp;Rudolf Weeber","doi":"10.1016/j.procs.2025.02.267","DOIUrl":"10.1016/j.procs.2025.02.267","url":null,"abstract":"<div><div>One of the milestones of the EuroHPC Centre of Excellence MultiXscale is to be able to run the EESSI test suite on at least two different architectures available on EuroHPC Supercomputers. Our initial efforts focused on making the test suite portable across two different supercomputers: Karolina and Vega (the CPU partitions of both are a Zen2 micro-architecture).</div><div>More recently we have spent time getting the same test suite working on a more “exotic” architecture, the ARM A64FX architecture of Deucalion (which was in pre-production at the time of the experiment). This has raised some additional complications for EESSI as CernVM-FS (which is used to distribute EESSI) was not yet natively available there.</div><div>We show the current scalability of the ESPResSo application using the portable test suite. ESPResSo is already known to have scalability issues for both multi-node and multi-GPU configurations which are currently being analysed in collaboration with the POP Centre of Excellence. The purpose of this effort was to ensure that we can quickly and automatically record the performance of the application across a range of EuroHPC systems (i.e. ESPResSo acts as a pilot application for the full test suite)</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"255 ","pages":"Pages 122-129"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the Drivers of Cryptocurrency Acceptance: An Empirical Study of Individual Adoption","authors":"Máté Hidegföldi,&nbsp;Gergely Laszlo Csizmazia,&nbsp;Justina Karpavičė","doi":"10.1016/j.procs.2025.02.151","DOIUrl":"10.1016/j.procs.2025.02.151","url":null,"abstract":"<div><div>Cryptocurrencies offer a novel approach to finance by eliminating the need for traditional banking and enabling secure, traceable, and internet-accessible peer-to-peer transactions. However, despite their advantages, cryptocurrencies face persistent trust issues and low levels of engagement and awareness. This research aims to investigate individuals’ behavioral intentions to use cryptocurrencies and identify factors influencing technology adoption. Employing a qualitative meta-analytic approach, a new predictive model was proposed, drawing from TAM, UTAUT, and IDT theories. A survey administered in Hungary utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) for data analysis, identifying social influence, facilitating conditions, and awareness as key factors impacting perceived ease of use (PEOU) and perceived usefulness (PE).</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"256 ","pages":"Pages 547-556"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}