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Digital twins (DT) are emerging as an extremely promising paradigm for run-time modelling and performability prediction of cyber-physical systems (CPS) in various domains. Although several different definitions and industrial applications of DT exist, ranging from purely visual three-dimensional models to predictive maintenance tools, in this paper, we focus on data-driven evaluation and prediction of critical dependability attributes such as safety. To that end, we introduce a conceptual framework based on autonomic systems to host DT run-time models based on a structured and systematic approach. We argue that the convergence between DT and self-adaptation is the key to building smarter, resilient and trustworthy CPS that can self-monitor, self-diagnose and-ultimately-self-heal. The conceptual framework eases dependability assessment, which is essential for the certification of autonomous CPS operating with artificial intelligence and machine learning in critical applications. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

Digital Twins are conceptualised in the academic technical discourse as real-time realistic digital representations of physical entities. Originating from product engineering, the Digital Twin quickly advanced into other fields, including the life sciences and earth sciences. Digital Twins are seen by the tech sector as the new promising tool for efficiency and optimisation, while governmental agencies see it as a fruitful means for improving decision-making to meet sustainability goals. A striking example of the latter is the European Commission who wishes to delegate a significant role to Digital Twins in addressing climate change and supporting Green Deal policy. As Digital Twins give rise to high expectations, ambitions, and are being entrusted important societal roles, it is crucial to critically reflect on the nature of Digital Twins. In this article, we therefore philosophically reflect on Digital Twins by critically analysing dominant conceptualisations, the assumptions underlying them, and their normative implications. We dissect the concept and argue that a Digital Twin does not merely fulfil the role of being a representation, but is in fact a steering technique used to direct a physical entity towards certain goals by means of multiple representations. Currently, this steering seems mainly fuelled by a reductionist approach focused on efficiency and optimisation. However, this is not the only direction from which a Digital Twin can be thought and, consequently, designed and deployed. We therefore set an agenda based on a critical understanding of Digital Twins that helps to draw out their beneficial potential, while addressing their potential issues.

The Soil Mechanics Laboratory has been traditionally an important issue in most courses on Soil Mechanics and Geotechnical Engineering. Nowadays, the available technology allows to build low cost systems that brings the experiment to the student in a simple manner. The paper describes a couple of examples including a physical model of a falling head permeameter and a simple device to perform direct shear tests. Students can be directly involved in the development of those equipments, so they become familiar with that technology which is simple and available at low cost. Simplifying and decreasing the cost of educational experiments reduces the gap between monitoring, automation, digitalization and real applications and eventually between theory and practice, and this is a fundamental aspect in the future development of Internet of Things., The financial support from the Spanish Research Agency through project BIA2017-82594-R is gratefully acknowledged., Postprint (published version)

The possibilities of 3D city models for the analysis of the built environment are increasingly explored, and there is a continuous development in improvements on their inner workings. They are also used more often on the web, for example for visualisation but it is also possible to query, analyse, and edit data in this way. In this case, the network becomes a potential new bottleneck in time performance. Especially when a 3D city model contains a lot of attributes, there is a rapid increase in file size when the area of study is expanded. This presents challenges in efficiency and in this thesis I focus on the improvement of the inner workings of 3D city models to attempt to relieve this problem, in specific for spreading and using them more efficiently on the web. By investigating and testing different compression techniques on 3D city models stored in the CityJSON format, I have attempted to relieve this problem. CityJSON is already more compact than CityGML and these techniques decrease the file sizes of datasets even further, allowing for faster transmission over a network. But on the other hand, additional steps are needed to process compressed files. The goal of using a compression technique is to result in a net speed gain, meaning that the time that is saved on download time should be larger than the additional time that it costs to process the file before transmission (compressing the data on the server) and after receival (decompressing the data in the client). There are compression techniques for both general and specific purposes, and I have used combinations of them. Amongst these are Draco compression, zlib, CBOR, and a self-created technique. Specific ones are used for the main characteristics that CityJSON datasets have, which are the JSON structure, feature attributes, and feature geometries. To assess the impact that different combinations of compression techniques have, I take uncompressed CityJSON performance as the baseline, https://github.com/jliempt/CityJSON-does-file-size-matter, Geomatics

Maintenance plays a vital part in the Circular Economy (CE). Repairing, upgrading and restoring façades extends their lifecycle and reduces waste. The European Union and Dutch government have set goals to reach a circular future: in 2050 the built environment must be 100% circular. However, this demands a tremendous change in the current maintenance system. Façades are often maintained for the service life of only 50 years. Therefore, the quality at end-of-life is usually low. Additionally, due to issues in information capture and management, the condition at end-of-life is also challenging to determine. Therefore, façades are discarded as waste, sometimes way before they are at their actual end-of-life. Furthermore, to increase the circular potential of façades, the existing maintenance system requires change. In this research, an investigation is done into how the current maintenance system can be facilitated towards circular maintenance utilizing digital technologies. Inspections on façade defects are currently carried out manually. Afterwards, this information is stored and maintained manually in real estate software. Visual inspections and information systems are prone to human errors. Therefore, a study is done into the automation of the existing maintenance system to facilitate communication between parties. The Digital Twin (DT) is a real-time computer-generated BIM-model of the mother building, using sensors and Internet of Things (IoT) to capture data. Existing detection technologies are compared with detection sensors from literature, like infrared thermography and fibre sensors. The influence of the recommended sensors on the existing maintenance process is determined. The output of this is a new maintenance system using a DT to increase the circularity potential of façades., Architecture, Urbanism and Building Sciences | Management in the Built Environment

Light-emitting diodes (LEDs) are among the key innovations that have revolutionized the lighting industry, due to their versatility in applications, higher reliability, longer lifetime, and higher efficiency compared with other light sources. The demand for increased lifetime and higher reliability has attracted a significant number of research studies on the prognostics and lifetime estimation of LEDs, ranging from the traditional failure data analysis to the latest degradation modeling and machine learning based approaches over the past couple of years. However, there is a lack of reviews that systematically address the currently evolving machine learning algorithms and methods for fault detection, diagnostics, and lifetime prediction of LEDs. To address those deficiencies, a review on the diagnostic and prognostic methods and algorithms based on machine learning that helps to improve system performance, reliability, and lifetime assessment of LEDs is provided. The fundamental principles, pros and cons of methods including artificial neural networks, principal component analysis, hidden Markov models, support vector machines, and Bayesian networks are presented. Finally, discussion on the prospects of the machine learning implementation from LED packages, components to system level reliability analysis, potential challenges and opportunities, and the future digital twin technology for LEDs lifetime analysis is provided., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Electronic Components, Technology and Materials

Since the shift from nomadic life, to aggregation in clusters or cities, humanity has faced epidemics. It is the cluster of people that provides the fertile environment for viruses to jump from one host to the next, generating an epidemic. The geographical distance among clusters is a barrier to the spread of the epidemics; traveling from one locale to another was the only way to continue the spread. In the past, the epidemics spread along the commerce, maritime, and land pathways. Travel was slow and sporadic so an epidemic took years to become a pandemic. Today we have both bigger clusters (megacities and cities that on average are much bigger than the ones of the past) and much faster and denser traffic among clusters. This fuels both epidemics and pandemics.

Since the shift from nomadic life, to aggregation in clusters or cities, humanity has faced epidemics. It is the cluster of people that provides the fertile environment for viruses to jump from one host to the next, generating an epidemic. The geographical distance among clusters is a barrier to the spread of the epidemics; traveling from one locale to another was the only way to continue the spread. In the past, the epidemics spread along the commerce, maritime, and land pathways. Travel was slow and sporadic so an epidemic took years to become a pandemic. Today we have both bigger clusters (megacities and cities that on average are much bigger than the ones of the past) and much faster and denser traffic among clusters. This fuels both epidemics and pandemics.

As organisations adapt to the rigorous demands set by global markets, the supply chains that constitute their logistics networks become increasingly complex. This often has a detrimental effect on the supply chain visibility within the organisation, which may in turn have a negative impact on the core business of the organisation. This paper aims to determine how organisations can benefit in terms of improving their logistical supply chain visibility by implementing a Digital Twin — an all-encompassing virtual representation of the physical assets that constitute the logistics system. Furthermore, challenges related to implementation and the necessary steps to overcome these challenges were examined. The results of the study are that Digital Twins may prove beneficial to organisations in terms of improving metrics of analytics, diagnostics, predictions and descriptions of physical assets. However, these benefits come with notable challenges — managing implementation and maintenance costs, ensuring proper information modelling, adopting new technology and leading the organisation through the changes that an implementation would entail. In conclusion, a Digital Twin is a powerful tool suitable for organisations where the benefits outweigh the challenges of the initial implementation. Therefore, careful consideration must be taken to ensure that the investment is worthwhile. Further research is required to determine the most efficient way of introducing a Digital Twin to a logistical supply chain., I takt med att organisationer anpassar sig till de hårda krav som ställs av den globala marknaden ökar också komplexiteten i deras logistiknätverk. Detta har ofta en negativ effekt på synligheten inom logistikkedjan i organisationen, vilken i sin tur kan ha en negativ påverkan på organisationens kärnverksamhet. Målet med denna studie är att utröna de fördelar som organisationer kan uppnå vad gäller att förbättra synligheten inom deras logistikkedjor genom att implementera en Digital Tvilling — en allomfattande virtuell representation av de fysiska tillgångar som utgör logistikkedjan. Resultaten av studien är att Digitala Tvillingar kan vara gynnsamma för organisationer när det gäller att förbättra analys, diagnostik, prognoser och beskrivningar av fysiska tillgångar. Implementationen medför dock utmaningar — hantering av implementations- och driftskostnader, utformning av informationsmodellering, anammandet av ny teknik och ledarskap genom förändringsarbetet som en implementering skulle innebära. Sammanfattningsvis är en Digital Tvilling ett verktyg som lämpar sig för organisationer där fördelarna överväger de utmaningar som tillkommer med implementationen. Därmed bör beslutet om en eventuell implementation endast ske efter noggrant övervägande. Vidare forskning behöver genomföras för att utröna den mest effektiva metoden för att introducera en Digital Tvilling till en logistikkedja.

Industry 4.0 is a concept that has attracted much research and development over the last decade. At its core is the need to connect physical devices with their digital representations which essentially means establishing a digital twin. Currently, the technological development of digital twins has gathered much attention while the organizational and business aspects are less investigated. In response, the suitability of enterprise modeling and capability management for the purpose of developing and management of business-driven digital twins has been analyzed. A number of requirements from literature are summarized and two industrial cases have been analyzed for the purpose of investigating how the digital twin initiatives emerge and what forces drive the start of their implementation projects. The findings are discussed with respect to how Enterprise Modeling and the Capability-Driven Development method are able to support the business motivation, design and runtime management of digital twins.

Industry 4.0 is a concept that has attracted much research and development over the last decade. At its core is the need to connect physical devices with their digital representations which essentially means establishing a digital twin. Currently, the technological development of digital twins has gathered much attention while the organizational and business aspects are less investigated. In response, the suitability of enterprise modeling and capability management for the purpose of developing and management of business-driven digital twins has been analyzed. A number of requirements from literature are summarized and two industrial cases have been analyzed for the purpose of investigating how the digital twin initiatives emerge and what forces drive the start of their implementation projects. The findings are discussed with respect to how Enterprise Modeling and the Capability-Driven Development method are able to support the business motivation, design and runtime management of digital twins.

This paper conceptually introduces VF-KDO (Virtual Factories with Knowledge-Driven Optimization, a research profile of the University of Skovde, Sweden, which is underway from 2018-2026. The goal of this research profile is to deliver radical innovations in manufacturing research essential to the design and operation of next-generation manufacturing systems. A unique concept proposed in VF-KDO is: knowledge extracted for decision support is achieved through systematically exploring, e.g., using advanced, interactive data analytics techniques on optimal solutions generated via many-objective optimizations on virtual factory models. As the word 'driven' means 'motivated' or 'manipulated', so does KDO have some two-fold meanings: (1) optimizations that aim at generating knowledge, not only mathematically optimal solutions; (2) knowledge-controlled optimizations, instead of some blind/black-box processes. It is this concept of KDO, combining with modular, virtual factory models at different levels, which distinguishes VF-KDO from other related research efforts found internationally and in Sweden. The cutting-edge research topics involved in the research profile and their synergy with the digitalization efforts of the 7 partner companies, in form of the development of an intelligent decision support system, can be used to improve the competiveness of the Swedish manufacturing industry by supporting their holistic, optimal and sustainable decision making., CC BY-NC 4.0

Since the shift from nomadic life, to aggregation in clusters or cities, humanity has faced epidemics. It is the cluster of people that provides the fertile environment for viruses to jump from one host to the next, generating an epidemic. The geographical distance among clusters is a barrier to the spread of the epidemics; traveling from one locale to another was the only way to continue the spread. In the past, the epidemics spread along the commerce, maritime, and land pathways. Travel was slow and sporadic so an epidemic took years to become a pandemic. Today we have both bigger clusters (megacities and cities that on average are much bigger than the ones of the past) and much faster and denser traffic among clusters. This fuels both epidemics and pandemics.

he article looks at the impact of digital transformation on the business environment and industrial relationships. Digital transformation and transition to Industry 4.0 radically changes the business environment of character and working methods, as well as significantly affects the boundaries of production, distribution and consumption. This trend shows both opportunities and threats simultaneously to all areas of activity, especially labor relations and business model of enterprises., Статья рассматривает влияние цифровой трансформации на бизнес среду и производственные отношения. Цифровая трансформация и переход к индустрии 4.0 радикально меняют бизнес-среду, характер и методы работы, а также существенно влияют на границы производства, распределения и потребления. Этот тренд показывает, как возможности, так и угрозы для всех сфер деятельности, прежде всего, трудовых отношений и бизнес-моделей деятельности предприятий.

Light-emitting diodes (LEDs) are among the key innovations that have revolutionized the lighting industry, due to their versatility in applications, higher reliability, longer lifetime, and higher efficiency compared with other light sources. The demand for increased lifetime and higher reliability has attracted a significant number of research studies on the prognostics and lifetime estimation of LEDs, ranging from the traditional failure data analysis to the latest degradation modeling and machine learning based approaches over the past couple of years. However, there is a lack of reviews that systematically address the currently evolving machine learning algorithms and methods for fault detection, diagnostics, and lifetime prediction of LEDs. To address those deficiencies, a review on the diagnostic and prognostic methods and algorithms based on machine learning that helps to improve system performance, reliability, and lifetime assessment of LEDs is provided. The fundamental principles, pros and cons of methods including artificial neural networks, principal component analysis, hidden Markov models, support vector machines, and Bayesian networks are presented. Finally, discussion on the prospects of the machine learning implementation from LED packages, components to system level reliability analysis, potential challenges and opportunities, and the future digital twin technology for LEDs lifetime analysis is provided., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Electronic Components, Technology and Materials

The present paper presents the first-of-its-kind digital twin for a furnace operating in flameless combustion conditions. A methodology combining data compression, by means of Proper Orthogonal Decomposition (POD), and interpolation, using Kriging, was developed to design physics-based, reduced-order models (ROMs) for the prediction of combustion data at unexplored operating conditions. Three-dimensional simulations with detailed chemistry were carried out, spanning a wide range of operating conditions in terms of fuel composition (methane-hydrogen mixtures from pure methane to pure hydrogen), equivalence ratio (from 0.7 to 1) and air injector diameter (to adjust the air jet entrainment). Based on the available simulations, a ROM was developed, to predict both spatial fields, local and integral values of thermochemical variables at working conditions not included in the ROM development. Results showed that the developed ROM could reliably predict the temperature and main chemical species distribution in the furnace with an overall error below 10%, proving the effectiveness of the approach for the development of digital twins of combustion systems. A remarkable accuracy was observed for the prediction of specific quantities, including wall temperatures, OH decay length, OH peak value and location and exhaust gas composition, including pollutants, with prediction errors always below 5%, showing the potential of the approach to develop soft sensors., SCOPUS: ar.j, info:eu-repo/semantics/published

This paper conceptually introduces VF-KDO (Virtual Factories with Knowledge-Driven Optimization, a research profile of the University of Skovde, Sweden, which is underway from 2018-2026. The goal of this research profile is to deliver radical innovations in manufacturing research essential to the design and operation of next-generation manufacturing systems. A unique concept proposed in VF-KDO is: knowledge extracted for decision support is achieved through systematically exploring, e.g., using advanced, interactive data analytics techniques on optimal solutions generated via many-objective optimizations on virtual factory models. As the word 'driven' means 'motivated' or 'manipulated', so does KDO have some two-fold meanings: (1) optimizations that aim at generating knowledge, not only mathematically optimal solutions; (2) knowledge-controlled optimizations, instead of some blind/black-box processes. It is this concept of KDO, combining with modular, virtual factory models at different levels, which distinguishes VF-KDO from other related research efforts found internationally and in Sweden. The cutting-edge research topics involved in the research profile and their synergy with the digitalization efforts of the 7 partner companies, in form of the development of an intelligent decision support system, can be used to improve the competiveness of the Swedish manufacturing industry by supporting their holistic, optimal and sustainable decision making., CC BY-NC 4.0

Digitalisation and automation of existing processes are key competitive factors for industry. Still, logistic operations often comprise manual effort, because the movement of goods and material places stringent requirements on the interactions between different systems, human-computer/robot-interaction as well as on changes in the operative processes. In general, the introduction and up-take of new enabling technologies, like the IoT, in complex systems evolved over decades, are challenging. The experience has shown that it is hard to assess all restrictions and interactions between new and old components before any new equipment or infrastructure is implemented and put in operation. This paper presents and discusses the usage of digital twins for supporting the decision-making processes in two different areas: Workstation design and logistics operation analysis. The results are based on tests and experiments carried out in a production logistics test-bed that includes physical devices, an IoT-infrastructure and simulation software. The digital twin is realised in a combination of using Unity and the simulation software IPS. The primary results show that there is no one-size fit all in terms of granularity of the underlying simulation model as well as for the reduction of reality in the digital twin, but the results also indicate that a context-aware digital twin supports the decision-making within a given scope., QC 20200929

Digital Twins (DTs) are described as the next wave in simulation, a critical component of Cyber Physical Systems (CPS), and a key enabler for da-ta-driven decision-making. Yet, the literature presents limited understanding about the characteristics of DTs and their relation to current simulation capabili-ties. Addressing this problem, the purpose of this study is to analyze the charac-teristics of DTs and simulation models for CPS in production logistics. This study reviews extant literature on DTs and presents findings from a single case study at a South Korean manufacturing company developing a DT including Discrete Event Simulation (DES). The findings of this study highlight the importance of DES in DTs focusing on increased production logistics performance. The results of this study indicate that the use of DES may promote the development of DTs, but be insufficient in the characterization of DTs for CPS in production logistics., QC 20200914, C-PALs

This paper conceptually introduces VF-KDO (Virtual Factories with Knowledge-Driven Optimization, a research profile of the University of Skovde, Sweden, which is underway from 2018-2026. The goal of this research profile is to deliver radical innovations in manufacturing research essential to the design and operation of next-generation manufacturing systems. A unique concept proposed in VF-KDO is: knowledge extracted for decision support is achieved through systematically exploring, e.g., using advanced, interactive data analytics techniques on optimal solutions generated via many-objective optimizations on virtual factory models. As the word 'driven' means 'motivated' or 'manipulated', so does KDO have some two-fold meanings: (1) optimizations that aim at generating knowledge, not only mathematically optimal solutions; (2) knowledge-controlled optimizations, instead of some blind/black-box processes. It is this concept of KDO, combining with modular, virtual factory models at different levels, which distinguishes VF-KDO from other related research efforts found internationally and in Sweden. The cutting-edge research topics involved in the research profile and their synergy with the digitalization efforts of the 7 partner companies, in form of the development of an intelligent decision support system, can be used to improve the competiveness of the Swedish manufacturing industry by supporting their holistic, optimal and sustainable decision making., CC BY-NC 4.0

As organisations adapt to the rigorous demands set by global markets, the supply chains that constitute their logistics networks become increasingly complex. This often has a detrimental effect on the supply chain visibility within the organisation, which may in turn have a negative impact on the core business of the organisation. This paper aims to determine how organisations can benefit in terms of improving their logistical supply chain visibility by implementing a Digital Twin — an all-encompassing virtual representation of the physical assets that constitute the logistics system. Furthermore, challenges related to implementation and the necessary steps to overcome these challenges were examined. The results of the study are that Digital Twins may prove beneficial to organisations in terms of improving metrics of analytics, diagnostics, predictions and descriptions of physical assets. However, these benefits come with notable challenges — managing implementation and maintenance costs, ensuring proper information modelling, adopting new technology and leading the organisation through the changes that an implementation would entail. In conclusion, a Digital Twin is a powerful tool suitable for organisations where the benefits outweigh the challenges of the initial implementation. Therefore, careful consideration must be taken to ensure that the investment is worthwhile. Further research is required to determine the most efficient way of introducing a Digital Twin to a logistical supply chain., I takt med att organisationer anpassar sig till de hårda krav som ställs av den globala marknaden ökar också komplexiteten i deras logistiknätverk. Detta har ofta en negativ effekt på synligheten inom logistikkedjan i organisationen, vilken i sin tur kan ha en negativ påverkan på organisationens kärnverksamhet. Målet med denna studie är att utröna de fördelar som organisationer kan uppnå vad gäller att förbättra synligheten inom deras logistikkedjor genom att implementera en Digital Tvilling — en allomfattande virtuell representation av de fysiska tillgångar som utgör logistikkedjan. Resultaten av studien är att Digitala Tvillingar kan vara gynnsamma för organisationer när det gäller att förbättra analys, diagnostik, prognoser och beskrivningar av fysiska tillgångar. Implementationen medför dock utmaningar — hantering av implementations- och driftskostnader, utformning av informationsmodellering, anammandet av ny teknik och ledarskap genom förändringsarbetet som en implementering skulle innebära. Sammanfattningsvis är en Digital Tvilling ett verktyg som lämpar sig för organisationer där fördelarna överväger de utmaningar som tillkommer med implementationen. Därmed bör beslutet om en eventuell implementation endast ske efter noggrant övervägande. Vidare forskning behöver genomföras för att utröna den mest effektiva metoden för att introducera en Digital Tvilling till en logistikkedja.

Digitalisation and automation of existing processes are key competitive factors for industry. Still, logistic operations often comprise manual effort, because the movement of goods and material places stringent requirements on the interactions between different systems, human-computer/robot-interaction as well as on changes in the operative processes. In general, the introduction and up-take of new enabling technologies, like the IoT, in complex systems evolved over decades, are challenging. The experience has shown that it is hard to assess all restrictions and interactions between new and old components before any new equipment or infrastructure is implemented and put in operation. This paper presents and discusses the usage of digital twins for supporting the decision-making processes in two different areas: Workstation design and logistics operation analysis. The results are based on tests and experiments carried out in a production logistics test-bed that includes physical devices, an IoT-infrastructure and simulation software. The digital twin is realised in a combination of using Unity and the simulation software IPS. The primary results show that there is no one-size fit all in terms of granularity of the underlying simulation model as well as for the reduction of reality in the digital twin, but the results also indicate that a context-aware digital twin supports the decision-making within a given scope., QC 20200929

Since the shift from nomadic life, to aggregation in clusters or cities, humanity has faced epidemics. It is the cluster of people that provides the fertile environment for viruses to jump from one host to the next, generating an epidemic. The geographical distance among clusters is a barrier to the spread of the epidemics; traveling from one locale to another was the only way to continue the spread. In the past, the epidemics spread along the commerce, maritime, and land pathways. Travel was slow and sporadic so an epidemic took years to become a pandemic. Today we have both bigger clusters (megacities and cities that on average are much bigger than the ones of the past) and much faster and denser traffic among clusters. This fuels both epidemics and pandemics.

This paper conceptually introduces VF-KDO (Virtual Factories with Knowledge-Driven Optimization, a research profile of the University of Skovde, Sweden, which is underway from 2018-2026. The goal of this research profile is to deliver radical innovations in manufacturing research essential to the design and operation of next-generation manufacturing systems. A unique concept proposed in VF-KDO is: knowledge extracted for decision support is achieved through systematically exploring, e.g., using advanced, interactive data analytics techniques on optimal solutions generated via many-objective optimizations on virtual factory models. As the word 'driven' means 'motivated' or 'manipulated', so does KDO have some two-fold meanings: (1) optimizations that aim at generating knowledge, not only mathematically optimal solutions; (2) knowledge-controlled optimizations, instead of some blind/black-box processes. It is this concept of KDO, combining with modular, virtual factory models at different levels, which distinguishes VF-KDO from other related research efforts found internationally and in Sweden. The cutting-edge research topics involved in the research profile and their synergy with the digitalization efforts of the 7 partner companies, in form of the development of an intelligent decision support system, can be used to improve the competiveness of the Swedish manufacturing industry by supporting their holistic, optimal and sustainable decision making., CC BY-NC 4.0

Digitalisation and automation of existing processes are key competitive factors for industry. Still, logistic operations often comprise manual effort, because the movement of goods and material places stringent requirements on the interactions between different systems, human-computer/robot-interaction as well as on changes in the operative processes. In general, the introduction and up-take of new enabling technologies, like the IoT, in complex systems evolved over decades, are challenging. The experience has shown that it is hard to assess all restrictions and interactions between new and old components before any new equipment or infrastructure is implemented and put in operation. This paper presents and discusses the usage of digital twins for supporting the decision-making processes in two different areas: Workstation design and logistics operation analysis. The results are based on tests and experiments carried out in a production logistics test-bed that includes physical devices, an IoT-infrastructure and simulation software. The digital twin is realised in a combination of using Unity and the simulation software IPS. The primary results show that there is no one-size fit all in terms of granularity of the underlying simulation model as well as for the reduction of reality in the digital twin, but the results also indicate that a context-aware digital twin supports the decision-making within a given scope., QC 20200929

Digitalisation and automation of existing processes are key competitive factors for industry. Still, logistic operations often comprise manual effort, because the movement of goods and material places stringent requirements on the interactions between different systems, human-computer/robot-interaction as well as on changes in the operative processes. In general, the introduction and up-take of new enabling technologies, like the IoT, in complex systems evolved over decades, are challenging. The experience has shown that it is hard to assess all restrictions and interactions between new and old components before any new equipment or infrastructure is implemented and put in operation. This paper presents and discusses the usage of digital twins for supporting the decision-making processes in two different areas: Workstation design and logistics operation analysis. The results are based on tests and experiments carried out in a production logistics test-bed that includes physical devices, an IoT-infrastructure and simulation software. The digital twin is realised in a combination of using Unity and the simulation software IPS. The primary results show that there is no one-size fit all in terms of granularity of the underlying simulation model as well as for the reduction of reality in the digital twin, but the results also indicate that a context-aware digital twin supports the decision-making within a given scope., QC 20200929

Since the shift from nomadic life, to aggregation in clusters or cities, humanity has faced epidemics. It is the cluster of people that provides the fertile environment for viruses to jump from one host to the next, generating an epidemic. The geographical distance among clusters is a barrier to the spread of the epidemics; traveling from one locale to another was the only way to continue the spread. In the past, the epidemics spread along the commerce, maritime, and land pathways. Travel was slow and sporadic so an epidemic took years to become a pandemic. Today we have both bigger clusters (megacities and cities that on average are much bigger than the ones of the past) and much faster and denser traffic among clusters. This fuels both epidemics and pandemics.

Digitalisation and automation of existing processes are key competitive factors for industry. Still, logistic operations often comprise manual effort, because the movement of goods and material places stringent requirements on the interactions between different systems, human-computer/robot-interaction as well as on changes in the operative processes. In general, the introduction and up-take of new enabling technologies, like the IoT, in complex systems evolved over decades, are challenging. The experience has shown that it is hard to assess all restrictions and interactions between new and old components before any new equipment or infrastructure is implemented and put in operation. This paper presents and discusses the usage of digital twins for supporting the decision-making processes in two different areas: Workstation design and logistics operation analysis. The results are based on tests and experiments carried out in a production logistics test-bed that includes physical devices, an IoT-infrastructure and simulation software. The digital twin is realised in a combination of using Unity and the simulation software IPS. The primary results show that there is no one-size fit all in terms of granularity of the underlying simulation model as well as for the reduction of reality in the digital twin, but the results also indicate that a context-aware digital twin supports the decision-making within a given scope., QC 20200929

This paper conceptually introduces VF-KDO (Virtual Factories with Knowledge-Driven Optimization, a research profile of the University of Skovde, Sweden, which is underway from 2018-2026. The goal of this research profile is to deliver radical innovations in manufacturing research essential to the design and operation of next-generation manufacturing systems. A unique concept proposed in VF-KDO is: knowledge extracted for decision support is achieved through systematically exploring, e.g., using advanced, interactive data analytics techniques on optimal solutions generated via many-objective optimizations on virtual factory models. As the word 'driven' means 'motivated' or 'manipulated', so does KDO have some two-fold meanings: (1) optimizations that aim at generating knowledge, not only mathematically optimal solutions; (2) knowledge-controlled optimizations, instead of some blind/black-box processes. It is this concept of KDO, combining with modular, virtual factory models at different levels, which distinguishes VF-KDO from other related research efforts found internationally and in Sweden. The cutting-edge research topics involved in the research profile and their synergy with the digitalization efforts of the 7 partner companies, in form of the development of an intelligent decision support system, can be used to improve the competiveness of the Swedish manufacturing industry by supporting their holistic, optimal and sustainable decision making., CC BY-NC 4.0

Nowadays, the growth of advanced technologies is paving the way for Industrial Control Systems (ICS) and making them more efficient and smarter. However, this makes ICS more connected to communication networks that provide a potential platform for attackers to intrude into the systems and cause damage and catastrophic consequences. In this paper, we propose implementing digital twins that have been equipped with an intrusion detection algorithm. Our novel algorithm is able to detect attacks in a timely manner and also diagnose the type of attack by classification of different types of attacks. With digital twins, which are a new concept in ICS, we have virtual replicas of physical systems so that they precisely mirror the internal behavior of the physical systems. So by placing the intrusion detection algorithm in digital twins, security tests can be done remotely without risking negative impacts on live systems.

A digital twin is a digital replica of a physical entity to which it is remotely connected. A digital twin can provide a rich representation of the corresponding physical entity and enables sophisticated control for various purposes. Although the concept of the digital twin is largely known, designing digital twins based systems has not yet been fully explored. In practice, digital twins can be applied in different ways leading to different architectural designs. To guide the architecture design process, we provide a pattern-oriented approach for architecting digital twinbased Internet of Things (IoT) systems. To this end, we propose a catalog of digital twin architecture design patterns that can be reused in the broad context of systems engineering. The patterns are described using the well-known documentation template and support the various phases in the systems engineering life cycle process. For illustrating the application of digital twin patterns, we adopt a case study in the agriculture and food domain.

Since the shift from nomadic life, to aggregation in clusters or cities, humanity has faced epidemics. It is the cluster of people that provides the fertile environment for viruses to jump from one host to the next, generating an epidemic. The geographical distance among clusters is a barrier to the spread of the epidemics; traveling from one locale to another was the only way to continue the spread. In the past, the epidemics spread along the commerce, maritime, and land pathways. Travel was slow and sporadic so an epidemic took years to become a pandemic. Today we have both bigger clusters (megacities and cities that on average are much bigger than the ones of the past) and much faster and denser traffic among clusters. This fuels both epidemics and pandemics.

The possibilities of 3D city models for the analysis of the built environment are increasingly explored, and there is a continuous development in improvements on their inner workings. They are also used more often on the web, for example for visualisation but it is also possible to query, analyse, and edit data in this way. In this case, the network becomes a potential new bottleneck in time performance. Especially when a 3D city model contains a lot of attributes, there is a rapid increase in file size when the area of study is expanded. This presents challenges in efficiency and in this thesis I focus on the improvement of the inner workings of 3D city models to attempt to relieve this problem, in specific for spreading and using them more efficiently on the web. By investigating and testing different compression techniques on 3D city models stored in the CityJSON format, I have attempted to relieve this problem. CityJSON is already more compact than CityGML and these techniques decrease the file sizes of datasets even further, allowing for faster transmission over a network. But on the other hand, additional steps are needed to process compressed files. The goal of using a compression technique is to result in a net speed gain, meaning that the time that is saved on download time should be larger than the additional time that it costs to process the file before transmission (compressing the data on the server) and after receival (decompressing the data in the client). There are compression techniques for both general and specific purposes, and I have used combinations of them. Amongst these are Draco compression, zlib, CBOR, and a self-created technique. Specific ones are used for the main characteristics that CityJSON datasets have, which are the JSON structure, feature attributes, and feature geometries. To assess the impact that different combinations of compression techniques have, I take uncompressed CityJSON performance as the baseline, https://github.com/jliempt/CityJSON-does-file-size-matter, Geomatics

Maintenance plays a vital part in the Circular Economy (CE). Repairing, upgrading and restoring façades extends their lifecycle and reduces waste. The European Union and Dutch government have set goals to reach a circular future: in 2050 the built environment must be 100% circular. However, this demands a tremendous change in the current maintenance system. Façades are often maintained for the service life of only 50 years. Therefore, the quality at end-of-life is usually low. Additionally, due to issues in information capture and management, the condition at end-of-life is also challenging to determine. Therefore, façades are discarded as waste, sometimes way before they are at their actual end-of-life. Furthermore, to increase the circular potential of façades, the existing maintenance system requires change. In this research, an investigation is done into how the current maintenance system can be facilitated towards circular maintenance utilizing digital technologies. Inspections on façade defects are currently carried out manually. Afterwards, this information is stored and maintained manually in real estate software. Visual inspections and information systems are prone to human errors. Therefore, a study is done into the automation of the existing maintenance system to facilitate communication between parties. The Digital Twin (DT) is a real-time computer-generated BIM-model of the mother building, using sensors and Internet of Things (IoT) to capture data. Existing detection technologies are compared with detection sensors from literature, like infrared thermography and fibre sensors. The influence of the recommended sensors on the existing maintenance process is determined. The output of this is a new maintenance system using a DT to increase the circularity potential of façades., Architecture, Urbanism and Building Sciences | Management in the Built Environment

The industry has during the past decades been changing towardsdigitalization at a rapid pace, adapting new frameworks and digitalsolutions, with the thrive to improve efficiency, and output quality.This thesis covers how a hyped industry concept, digital twin, incontext of smart manufacturing, could be applied in this changingclimate. Focus is put on what strategies a Product LifecycleManagement (PLM) principal could adapt when approaching theutilization of digital twins, in a customer setting. The research project, using a qualitative exploration format,incorporated a thorough review of journal articles and standards,interconnected with conducted interviews with industry experts, inorder to develop two strategies on how to approach the conceptof digital twins. The two-part strategies were formed on the basis of Mintzberg(1978) emergent and deliberate strategies. The former, consisted ofIDEF0 function modeling diagramming, where a digital twin businessprocess was portrayed, based on the literature review and interviewdata. The latter, approached the digital twin application challenge ina theoretical manner, based on the concept’s high risk anduncertainty, incorporating organizational structure theory andinnovation theory. The two strategies showed two different pathsto approach the digital twin phenomena and how to, boththeoretically and practically, adapt digital twins in a customer setting.

The industry has during the past decades been changing towardsdigitalization at a rapid pace, adapting new frameworks and digitalsolutions, with the thrive to improve efficiency, and output quality.This thesis covers how a hyped industry concept, digital twin, incontext of smart manufacturing, could be applied in this changingclimate. Focus is put on what strategies a Product LifecycleManagement (PLM) principal could adapt when approaching theutilization of digital twins, in a customer setting. The research project, using a qualitative exploration format,incorporated a thorough review of journal articles and standards,interconnected with conducted interviews with industry experts, inorder to develop two strategies on how to approach the conceptof digital twins. The two-part strategies were formed on the basis of Mintzberg(1978) emergent and deliberate strategies. The former, consisted ofIDEF0 function modeling diagramming, where a digital twin businessprocess was portrayed, based on the literature review and interviewdata. The latter, approached the digital twin application challenge ina theoretical manner, based on the concept’s high risk anduncertainty, incorporating organizational structure theory andinnovation theory. The two strategies showed two different pathsto approach the digital twin phenomena and how to, boththeoretically and practically, adapt digital twins in a customer setting.

Personalized medicine uses fine grained information on individual persons, to pinpoint deviations from the normal. ‘Digital Twins’ in engineering provide a conceptual framework to analyze these emerging data-driven health care practices, as well as their conceptual and ethical implications for therapy, preventative care and human enhancement. Digital Twins stand for a specific engineering paradigm, where individual physical artifacts are paired with digital models that dynamically reflects the status of those artifacts. When applied to persons, Digital Twins are an emerging technology that builds on in silico representations of an individual that dynamically reflect molecular status, physiological status and life style over time. We use Digital Twins as the hypothesis that one would be in the possession of very detailed bio-physical and lifestyle information of a person over time. This perspective redefines the concept of ‘normality’ or ‘health,’ as a set of patterns that are regular for a particular individual, against the backdrop of patterns observed in the population. This perspective also will impact what is considered therapy and what is enhancement, as can be illustrated with the cases of the ‘asymptomatic ill’ and life extension via anti-aging medicine. These changes are the consequence of how meaning is derived, in case measurement data is available. Moral distinctions namely may be based on patterns found in these data and the meanings that are grafted on these patterns. Ethical and societal implications of Digital Twins are explored. Digital Twins imply a data-driven approach to health care. This approach has the potential to deliver significant societal benefits, and can function as a social equalizer, by allowing for effective equalizing enhancement interventions. It can as well though be a driver for inequality, given the fact that a Digital Twin might not be an accessible technology for everyone, and given the fact that patterns identified across a po, Ethics & Philosophy of Technology, Values Technology and Innovation

Personalized medicine uses fine grained information on individual persons, to pinpoint deviations from the normal. ‘Digital Twins’ in engineering provide a conceptual framework to analyze these emerging data-driven health care practices, as well as their conceptual and ethical implications for therapy, preventative care and human enhancement. Digital Twins stand for a specific engineering paradigm, where individual physical artifacts are paired with digital models that dynamically reflects the status of those artifacts. When applied to persons, Digital Twins are an emerging technology that builds on in silico representations of an individual that dynamically reflect molecular status, physiological status and life style over time. We use Digital Twins as the hypothesis that one would be in the possession of very detailed bio-physical and lifestyle information of a person over time. This perspective redefines the concept of ‘normality’ or ‘health,’ as a set of patterns that are regular for a particular individual, against the backdrop of patterns observed in the population. This perspective also will impact what is considered therapy and what is enhancement, as can be illustrated with the cases of the ‘asymptomatic ill’ and life extension via anti-aging medicine. These changes are the consequence of how meaning is derived, in case measurement data is available. Moral distinctions namely may be based on patterns found in these data and the meanings that are grafted on these patterns. Ethical and societal implications of Digital Twins are explored. Digital Twins imply a data-driven approach to health care. This approach has the potential to deliver significant societal benefits, and can function as a social equalizer, by allowing for effective equalizing enhancement interventions. It can as well though be a driver for inequality, given the fact that a Digital Twin might not be an accessible technology for everyone, and given the fact that patterns identified across a po, Ethics & Philosophy of Technology, Values Technology and Innovation

peer-reviewed, Digital twins (DTs) is a promising technology in the revolution of the industry and essential for Industry 4.0. DTs play a vital role in improving distributed manufacturing, providing up-to-date operational data representation of physical assets, supporting decision-making, and avoiding the potential risks in distributed manufacturing systems. Furthermore, DTs need to collaborate within distributed manufacturing systems to predict the risks and reach consensus-based decision-making. However, DTs collaboration suffers from single failure due to attack and connection in a centralized manner, data interoperability, authentication, and scalability. To overcome the above challenges, we have discussed the major high-level requirements for the DTs collaboration. Then, we have proposed a conceptual framework to fulfill the DTs collaboration requirements by using the combination of blockchain, predictive analysis techniques, and DTs technologies. The proposed framework aims to empower more intelligence DTs based on blockchain technology. In particular, we propose a concrete ledger-based collaborative DTs framework that focuses on real-time operational data analytics and distributed consensus algorithms. Furthermore, we describe how the conceptual framework can be applied using smart transportation system use cases, i.e., smart logistics and railway predictive maintenance. Finally, we highlighted the future direction to guide interested researchers in this interesting area.