Quo vadis industry 4.0? Position, trends, and challenges

Industry 4.0 vision and its mandated digital transformation are radically reshaping the way business is carried out and the way overall industrial processes and collaborations are operating. In this work, the objective is to analyze the current level of adoption of Industry 4.0, via the footprint available in industrial and academic research works. The analysis performed reveals insights on how Industry 4.0 has impacted and is still influencing research and innovation in industrial systems, services, and business approaches. It also reveals pertinent trends on key enabling features, technologies and challenges associated with this 4th industrial revolution, mainly focusing on the pathways for wider industrial adoption of Industry 4.0-compliant technologies and solutions.info:eu-repo/semantics/publishedVersio

Automatic Performance Testing of Maritime Simulation Models

Simulation models are essential tools in the maritime industry for predicting ship behavior and interactions in changing marine environments. This report explores the importance of these models and outlines how they might improve ship operations, design, and safety. It highlights the need for ongoing updates to guarantee their dependability. The performance of vessels must be improved while emissions and expenses are reduced since maritime transportation is essential for global trade. Simulation models provide useful insights for this project. This thesis provides an intelligent way for automating testing of marine simulation models in the Open Simulation Platform (OSP) environment by utilizing deep learning techniques. With the help of artificial intelligence and computer algorithms, this methodology aims to increase testing accuracy while lowering costs. Customized tests for simulation models are developed using generative adversarial networks (GANs), which mimic real-world behaviors. Although the computational requirements present difficulties, the accuracy and dependability shown by GANs surpass those of traditional techniques. This thesis emphasizes the usefulness of deep learning in maritime software testing, improving performance evaluation through careful investigation. Extending the variety of scenarios and improving the test creation procedures are two future directions. In the end, this project helps the marine industry advance toward improved effectiveness, environmental awareness, and safety. The conclusions drawn from the paper highlight the good potential of cutting-edge technology in creating a future for maritime transportation that is sustainable

Boundary State Generation for Testing and Improvement of Autonomous Driving Systems

Recent advances in Deep Neural Networks (DNNs) and sensor technologies are enabling autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However, assessing their dependability remains a critical concern. State-of-the-art ADS testing approaches modify the controllable attributes of a simulated driving environment until the ADS misbehaves. Such approaches have two main drawbacks: (1) modifications to the simulated environment might not be easily transferable to the in-field test setting (e.g., changing the road shape); (2) environment instances in which the ADS is successful are discarded, despite the possibility that they could contain hidden driving conditions in which the ADS may misbehave. In this paper, we present GenBo (GENerator of BOundary state pairs), a novel test generator for ADS testing. GenBo mutates the driving conditions of the ego vehicle (position, velocity and orientation), collected in a failure-free environment instance, and efficiently generates challenging driving conditions at the behavior boundary (i.e., where the model starts to misbehave) in the same environment. We use such boundary conditions to augment the initial training dataset and retrain the DNN model under test. Our evaluation results show that the retrained model has up to 16 higher success rate on a separate set of evaluation tracks with respect to the original DNN model

Innovation Policy Roadmapping for the Future Finnish Smart City Digital Twins : Towards Finland National Digital Twin Programme

Smart City Digital Twins (SCDTs) emerge as a transforming concept with the ability to redefine the future of cities in the fast-paced evolving landscape of urban development. This qualitative futures research explores thoroughly into the complex interaction of socio-technical dynamics in the Finnish setting, investigating the several ways SCDTs might revolutionise urban spaces and create resilience. By utilizing Innovation Policy Roadmapping (IPRM) method for the first time on SCDTs, it reveals the diverse capacities of SCDTs across domains such as urban planning, scenario developing, What-IF analysis, and public involvement through a rigorous examination of academic literature and multi-level analysis of expert interviews. The research emphasises the critical role of policymakers and sectoral actors in building an environment that allows Finnish SCDTs to survive in the face of technological improvements. Furthermore, it emphasises the convergence of SCDTs and Futures Studies approaches, giving a visionary path to adaptable and forward-thinking urban futures. The contributions of this study extend beyond the scope of Finnish SCDTs, giving inspiration for sustainable smart city transformations, potential foundational insights towards Finland National Digital Twin Programme and paving the way for the incorporation of futures studies methodologies and digital twins to mitigate uncertainties and create resilient urban futures. Longitudinal impact assessments, real-time citizen-centric foresight applications via SCDT, and the investigation of SCDTs' role in disaster mitigation and social well-being are among the identified future research directions, providing a comprehensive roadmap for leveraging SCDTs as transformative tools for building sustainable urban futures

What are the key multidimensional success criteria required for reducing LCOE through digital transformation in offshore wind farms?

Formålet med denne studien er å undersøke de flerdimensjonale suksesskriteriene som er avgjørende for å redusere energikostnaden også kjent som Levlized cost of Energy (LCOE) gjennom digital transformasjon innenfor offshore vind prosjekter. For å besvare problemstilling vil studien sette søkelys på fire underspørsmål som omhandler: (1) For å sikre operational excellence og tilpasning til FNs bærekraftsmål gjennom digital transformasjon: Hvilke suksessfaktorer må være på plass? (2) Er data tilgjengelig for bruk til den digital transformasjon? (3) Hvordan kan man muliggjør optimal Grid Integration av vindparken? (4) Kan man utnytte digitale verktøy for å redusere LCOE i en havvindpark? Studien fremhever den uunnværlige rollen av teknologi i form av digitale verktøy og data, som spiller som katalysatorer for å styrke operasjonell effektivitet og maksimere verdiskaping i offshore vindenergisektoren. Studien er gjennomført som kvalitativ Case-studier analyse i form av ti individuelle dybdeintervjuer med deltakere fra ulike selskaper i verdikjeden til offshore vind industri. Studien undersøker den betydelige påvirkningen FNs bærekraftsmål har på utviklingen av offshore vindprosjekter, samt den vitale rollen operational excellence har for å lykkes. Den vurderer om offshore vind industrien er klar for Industri 5.0, dens evne til å redusere LCOE, og dens innflytelse på sektorens fremtid. Funnene understreker betydningen av tilgjengelig data, optimalisert effektivitet, og bruk av sanntidsdata for å forbedre sikkerhet, bærekraft og effektiv energiproduksjon i vindparker. Videre dykker studien ned i implementeringen av digital transformasjon, og viser til hvordan digitale verktøy og automatisering, sammen med menneskelig inngripen, driver informert beslutningstaking. Funnene legger vekt på nødvendigheten av datasamarbeid, kunnskapsdeling, og kompetent personell for å fremme industriell vekst, samtidig som det opprettholdes en balanse mellom kompleksitet og kompetanse, og utforsker avansert digital tvilling-teknologi og hvordan det kan påvirke i redusering av LCOE. Studien tilbyr verdifull innsikt for interessenter og hjelper til med å håndtere utfordringer og muligheter i digital transformasjon av offshore vindparker. Den fremhever offshore vinindustriens avgjørende rolle i utviklingen av renere, effektive energisystemer, og støtter en bærekraftig og fremgangsrik fremtid.This purpose of this study is to thoroughly examine the multidimensional success criteria crucial in reducing the levelized cost of energy (LCOE) through digital transformation within the context of offshore wind farm projects. To help answer the research question, this study will focus on four preliminary research questions: (1) To ensure Operational Excellence and Alignment with UN SDGs through Digital Transformation: What success factors need to be in place? (2) Is Data available to be used to enable Digital Transformation? (3) How do you enable optimal Grid Integration of the wind park? (4) Can you leverage digital tools to reduce LCOE in an offshore wind farm? The research spotlights the indispensable role of technology in form of digital tools and data, as catalysts for bolstering operational efficiency and maximizing value creation in the offshore wind energy sector. The study has been carried out as a qualitative case study analysis in the form of ten individual in-depth interviews with participants from various companies in the value chain of the offshore wind industry. The study investigates the substantial impact of United Nations (UN) sustainability goals on offshore wind project development and the vital role of operational excellence. It evaluates the industry's preparedness for Industry 5.0, its capacity to reduce LCOE, and its influence on the sector's future. The research and findings underscore the significance of accessible data, optimized efficiency, and real-time data utilization to enhance safety, sustainability, and energy production in wind farms. Additionally, the research delves into Industry 5.0's implementation, demonstrating how digital tools and automation, combined with human input, drive informed decision-making. The findings emphasize the necessity for data collaboration, knowledge sharing, and skilled personnel to foster industry growth while maintaining a balance between complexity and competence and explores advanced digital twin technology and how it can influence in reducing LCOE. The study offers valuable insights for stakeholders and aids in addressing challenges and opportunities in offshore wind farm digital transformation. It accentuates the offshore wind industry's pivotal role in advancing cleaner, efficient energy systems, promoting a sustainable and prosperous future

Risk Analysis for Smart Cities Urban Planners: Safety and Security in Public Spaces

Christopher Alexander in his famous writings "The Timeless Way of Building" and "A pattern language" defined a formal language for the description of a city. Alexander developed a generative grammar able to formally describe complex and articulated concepts of architecture and urban planning to define a common language that would facilitate both the participation of ordinary citizens and the collaboration between professionals in architectural and urban planning. In this research, a similar approach has been applied to let two domains communicate although they are very far in terms of lexicon, methodologies and objectives. These domains are urban planning, urban design and architecture, seen as the first domain both in terms of time and in terms of completeness of vision, and the one relating to the world of engineering, made by innumerable disciplines. In practice, there is a domain that defines the requirements and the overall vision (the first) and a domain (the second) which implements them with real infrastructures and systems. To put these two worlds seamlessly into communication, allowing the concepts of the first world to be translated into those of the second, Christopher Alexander’s idea has been followed by defining a common language. By applying Essence, the software engineering formal descriptive theory, using its customization rules, to the concept of a Smart City, a common language to completely trace the requirements at all levels has been defined. Since the focus was on risk analysis for safety and security in public spaces, existing risk models have been considered, evidencing a further gap also within the engineering world itself. Depending on the area being considered, risk management models have different and siloed approaches which ignore the interactions of one type of risk with the others. To allow effective communication between the two domains and within the engineering domain, a unified risk analysis framework has been developed. Then a framework (an ontology) capable of describing all the elements of a Smart City has been developed and combined with the common language to trace the requirements. Following the philosophy of the Vienna Circle, a creative process called Aufbau has then been defined to allow the generation of a detailed description of the Smart City, at any level, using the common language and the ontology above defined. Then, the risk analysis methodology has been applied to the city model produced by Aufbau. The research developed tools to apply such results to the entire life cycle of the Smart City. With these tools, it is possible to understand how much a given architectural, urban planning or urban design requirement is operational at a given moment. In this way, the narration can accurately describe how much the initial requirements set by architects, planners and urban designers and, above all, the values required by stakeholders, are satisfied, at any time. The impact of this research on urban planning is the ability to create a single model between the two worlds, leaving everyone free to express creativity and expertise in the appropriate forms but, at the same time, allowing both to fill the communication gap existing today. This new way of planning requires adequate IT tools and takes the form, from the engineering side, of harmonization of techniques already in use and greater clarity of objectives. On the side of architecture, urban planning and urban design, it is instead a powerful decision support tool, both in the planning and operational phases. This decision support tool for Urban Planning, based on the research results, is the starting point for the development of a meta-heuristic process using an evolutionary approach. Consequently, risk management, from Architecture/Urban Planning/Urban Design up to Engineering, in any phase of the Smart City’s life cycle, is seen as an “organism” that evolves.Christopher Alexander nei suoi famosi scritti "The Timeless Way of Building" e "A pattern language" ha definito un linguaggio formale per la descrizione di una città, sviluppando una grammatica in grado di descrivere formalmente concetti complessi e articolati di architettura e urbanistica, definendo un linguaggio comune per facilitare la partecipazione dei comuni cittadini e la collaborazione tra professionisti. In questa ricerca, un approccio simile è stato applicato per far dialogare due domini sebbene siano molto distanti in termini di lessico, metodologie e obiettivi. Essi sono l'urbanistica, l'urban design e l'architettura, visti come primo dominio sia in termini di tempo che di completezza di visione, e quello del mondo dell'ingegneria, con numerose discipline. In pratica, esiste un dominio che definisce i requisiti e la visione d'insieme (il primo) e un dominio (il secondo) che li implementa con infrastrutture e sistemi reali. Per metterli in perfetta comunicazione, permettendo di tradurre i concetti del primo in quelli del secondo, si è seguita l'idea di Alexander definendo un linguaggio. Applicando Essence, la teoria descrittiva formale dell'ingegneria del software al concetto di Smart City, è stato definito un linguaggio comune per tracciarne i requisiti a tutti i livelli. Essendo il focus l'analisi dei rischi per la sicurezza negli spazi pubblici, sono stati considerati i modelli di rischio esistenti, evidenziando un'ulteriore lacuna anche all'interno del mondo dell'ingegneria stessa. A seconda dell'area considerata, i modelli di gestione del rischio hanno approcci diversi e isolati che ignorano le interazioni di un tipo di rischio con gli altri. Per consentire una comunicazione efficace tra i due domini e all'interno del dominio dell'ingegneria, è stato sviluppato un quadro di analisi del rischio unificato. Quindi è stato sviluppato un framework (un'ontologia) in grado di descrivere tutti gli elementi di una Smart City e combinato con il linguaggio comune per tracciarne i requisiti. Seguendo la filosofia del Circolo di Vienna, è stato poi definito un processo creativo chiamato Aufbau per consentire la generazione di una descrizione dettagliata della Smart City, a qualsiasi livello, utilizzando il linguaggio comune e l'ontologia sopra definita. Infine, la metodologia dell'analisi del rischio è stata applicata al modello di città prodotto da Aufbau. La ricerca ha sviluppato strumenti per applicare tali risultati all'intero ciclo di vita della Smart City. Con questi strumenti è possibile capire quanto una data esigenza architettonica, urbanistica o urbanistica sia operativa in un dato momento. In questo modo, la narrazione può descrivere con precisione quanto i requisiti iniziali posti da architetti, pianificatori e urbanisti e, soprattutto, i valori richiesti dagli stakeholder, siano soddisfatti, in ogni momento. L'impatto di questa ricerca sull'urbanistica è la capacità di creare un modello unico tra i due mondi, lasciando ognuno libero di esprimere creatività e competenza nelle forme appropriate ma, allo stesso tempo, permettendo ad entrambi di colmare il gap comunicativo oggi esistente. Questo nuovo modo di progettare richiede strumenti informatici adeguati e si concretizza, dal lato ingegneristico, in un'armonizzazione delle tecniche già in uso e in una maggiore chiarezza degli obiettivi. Sul versante dell'architettura, dell'urbanistica e del disegno urbano, è invece un potente strumento di supporto alle decisioni, sia in fase progettuale che operativa. Questo strumento di supporto alle decisioni per la pianificazione urbana, basato sui risultati della ricerca, è il punto di partenza per lo sviluppo di un processo meta-euristico utilizzando un approccio evolutivo