Physical Counterpart Research Articles

Digital twin‐based framework for wireless multimodal interactions over long distance

SummaryWireless multimodal interactions over long distance (WMILD) would give rise to numerous thrilling applications, such as remote touching and immersive teleoperations. However, long distances can induce large propagation delays, which makes it difficult to meet the ultra‐low latency requirements in haptic‐visual interactions. Considering existing works mainly focused on the wireless access part, this paper designs an end‐to‐end framework for general WMILD applications based on the digital twin (DT) technology and proposes an intelligent resource allocation and parameter compression scheme to guarantee WMILD performance under constraint network resources. In the framework, user device can acquire real‐time remote interactions by performing local interactions with nearby base station (BS), where a DT of the remote side is deployed to predict the remote haptic‐visual feedbacks. A reliable DT updating process is carefully designed to guarantee the DT accurately model its dynamic physical counterpart. To optimize the updating reliability, we formulate the resource allocation and parameter compression to be a constraint‐Markov decision problem, under the constraints on energy consumption, multimodal interactions and updating latencies. Then, a safe deep reinforcement learning algorithm is proposed to adapt resources and compression according to the dynamic DT updating workload, multimodal data‐streams and remote transmission capacities. Simulation shows the framework can achieve high updating reliability compared with baselines.

General purpose digital twin framework using digital shadow and distributed system concepts

Digital twin (DT) is an emerging concept in the Industry 4.0 era. It integrates intelligence into industrial processes. The broadness of DT’s concept allows for multiple definitions and designs to be labeled as DTs. However, general-purpose DTs are still out of reach due to the lack of consensus, tool dependency, and the focus on system integration. In this paper, a general-purpose DT framework is proposed by utilizing distributed system concepts. Data acquisition, system modeling, and intelligence applications are modularized in order to enable framework generality. Digital shadow (DS) is implemented as a core component of DT, which provides a real-time virtual replica of its physical counterpart. DT applications utilize DS models for recommending intelligent decisions. A systematic modeling approach is designed by differentiating between DS models and DT applications. The proposed approach defines the model flow based on its roles and objectives. The proposed DT framework is applied to a semiconductor production use case. For this system, the proposed DT provides insights into the production system’s behavior and estimates product completion time. Operators dispatch work-in-progress products using DT recommendations based on the equipment setup and the production system’s status.

Defining a Digital Twin: A Data Science-Based Unification

The concept of a digital twin (DT) has gained significant attention in academia and industry because of its perceived potential to address critical global challenges, such as climate change, healthcare, and economic crises. Originally introduced in manufacturing, many attempts have been made to present proper definitions of this concept. Unfortunately, there remains a great deal of confusion surrounding the underlying concept, with many scientists still uncertain about the distinction between a simulation, a mathematical model and a DT. The aim of this paper is to propose a formal definition of a digital twin. To achieve this goal, we utilize a data science framework that facilitates a functional representation of a DT and other components that can be combined together to form a larger entity we refer to as a digital twin system (DTS). In our framework, a DT is an open dynamical system with an updating mechanism, also referred to as complex adaptive system (CAS). Its primary function is to generate data via simulations, ideally, indistinguishable from its physical counterpart. On the other hand, a DTS provides techniques for analyzing data and decision-making based on the generated data. Interestingly, we find that a DTS shares similarities to the principles of general systems theory. This multi-faceted view of a DTS explains its versatility in adapting to a wide range of problems in various application domains such as engineering, manufacturing, urban planning, and personalized medicine.

Building Information Modeling (BIM 6D) and Its Application to Thermal Loads Calculation in Retrofitting

The purpose of this study is to propose optimal actions to improve the energy efficiency of large office buildings in tropical regions with cooling systems, while ensuring the users’ comfort at a reasonable cost. In tropical climates, the building envelope plays a crucial role in saving thermal energy as buildings are exposed to significant climatic impacts and require a significant amount of energy to achieve optimal indoor comfort conditions. In this context, BIM-3D simulation is considered to be effective since it can provide results very similar to those of its physical counterpart, which can be useful for decision making. For this purpose, a public building in Costa Rica is used as a case study, which is modeled in Revit 2019 to obtain a BIM-3D model and simulate its thermal behavior using the BIM tools of the referred software. The architectural characteristics are evaluated in the climatic context of the building, and results are simulated with different configured materials. The obtained results lead to the conclusion that simulation together the previous economic analysis is a valuable decision-making tool for design, enabling significant savings during construction and subsequent building use.

A human-centered framework to update digital twins

Developing a digital twin (DT) involves establishing (1) a predictive capability (a model) relevant to the application, (2) means to collect data from the physical counterpart, and (3) means to apply the collected data to the model. Ideally, with these three goals achieved, long periods of steady-state use of the DT might be interrupted only by failure of the sensors used to collect data from the physical counterpart. In reality, however, it can be difficult to confirm that the DT system occupies this comfortable steady-state position. Assessing uncertainty in the predictive model, and ensuring the relevance of data collected from the physical counterpart are design-time activities with unclear termination points. Distinguishing sensed change in the physical counterpart from sensor failure is a persistent challenge. In this short paper we describe early work towards a human-centered framework to establish, refine, and update digital twins. Condition-based maintenance and gear backlash in production equipment are used as examples.

DIGITAL SERVICES AND PORTALS FOR MIGRANTS IN THE SOCIAL SECURITY SYSTEM

DIGITAL SERVICES AND PORTALS FOR MIGRANTS IN THE SOCIAL SECURITY SYSTEM

Automated digital twin generation of manufacturing systems with complex material flows: graph model completion

Industry 4.0 determined the emergence of technologies that enable data-driven production planning and control approaches. A digital model can be used to make decisions based on the current state of a manufacturing system, and its efficacy strictly depends on the capability to correctly represent the physical counterpart at any time. Automated model generation techniques such as process mining can significantly accelerate the development of digital twins for manufacturing systems. However, complex production environments are characterized by the convergence of different material and information flows. The corresponding data logs present multiple part identifiers, resulting in the wrong finding of the system structure with traditional process mining techniques. This paper describes the problem of discovering manufacturing systems with complex material flows, such as assembly lines. An algorithm is proposed for the proper digital model generation, aided by the new concept of object-centric process mining. The proposed approach has been applied successfully to two test cases and a real manufacturing system. The results show the applicability of the proposed technique to realistic settings.

Resolviendo ecuaciones diferenciales ordinarias con Symbolic Math Toolbox™ (Matlab) y SymPy (Python)

This paper shows solutions of ordinary differential equations (EDOS) obtained by using two symbolic packages: Symbolic Math Toolbox™ (Matlab) and SymPy (Python). The basic instructions to obtain solutions of both packages are explained step by step, through a group of examples from a traditional ordinary differential equations course. Differential equations that are solved with methods such as: separable variables, linear equations, indeterminate coefficients, variation of parameters, power series, Laplace transform, and numerical solutions are included. By means of the symbolic computation carried out with these packages it is possible to obtain the solution of linear systems, as well as the visualization of the direction field of a differential equation or of a non-linear system of differential equations. The main contribution of this work consists in providing the reader with a practical guide that allows him to start the study of differential equations assisted by Symbolic Math Toolbox™ or SymPy. Among the benefits of using these computational tools in teaching and/or learning practices, it is shown how the use of symbolic or numerical computation saves us effort in the computation of tedious calculations; focusing attention on important ideas and concepts such as: the relationship between the mathematical model and its physical counterpart, asymptotic behavior and qualitative analysis of the solutions.

ENIGMA: An explainable digital twin security solution for cyber–physical systems

Digital Twins (DTs), being the virtual replicas of their physical counterparts, share valuable knowledge of the underlying physical processes and act as data acquisition and dissemination sources to Cyber–Physical System (CPS). Moreover, without obstructing the ongoing operations, DTs also provide an assessment platform for evaluating the operational behavior and security of the CPS. Therefore, they become a potential source of data breaches and a broad attack surface for attackers to launch covert attacks. To detect and mitigate security loopholes in DTs, one of the potential solutions is to leverage a gamification approach that can assess the security level of DTs while providing security analysts with a controlled and supportive virtual training environment. Artificial Intelligence/Machine Learning (AI/ML)-based approaches can complement the idea of security orchestration and automation in the gamification approach. However, AI/ML-based DTs security solutions are generally constrained by the lack of transparency of AI operations, which results in less confidence in the decisions made by the AI models. To address the explainable security challenges of DTs, this article proposes a gamification approach called sEcuriNg dIgital twins through GaMification Approach (ENIGMA). While leveraging DTs as an offensive security platform, ENIGMA provides gaming scenarios to assess DTs’ security and train security analysts. The game players within ENIGMA are humans (the attacker team) and AI agents (the defender team). Furthermore, ENIGMA is supported by an eXplainable AI (XAI)-based DT security assessment model that explains the decisions made based on the SHAP values by the AI model on attack vectors for the defender team, i.e., the AI agent. The SHAP values illustrate the contribution of different features towards predicting the outcome of attack vectors. This explanation can help security analysts to take security measures based on reasoned and trustworthy decisions. Finally, experimental validation has been carried out to demonstrate the viability of ENIGMA.

Procedural content generation method for creating 3D graphic assets in Digital Twin

The concept of Digital Twin was invented not only for a static virtual mimic of reality, but also means an evolving digital copy which could stay consistent with its physical counterpart. The life-cycle management of a product or a system could not be completed without dynamic feature, while conventional methods to create graphic assets especially 3D models for Digital Twins are still limited. In this paper, Procedural Content Generation is introduced as a potential technique which could reduce difficulty for the creation and modification of 3D graphic assets of Digital Twins. A trial project and two validation cases are executed to verify the proposed method.

Digital Twin Based SUDIHA Architecture to Smart Shopfloor Scheduling

Standing on the brink of the fourth industrial revolution, Cyber Physical Systems (CPS) are considered the basic components of the Smart Factory. One important challenge in cyber physical production systems is dynamic scheduling that can handle random disruptions such as failures, raw material shortages and quality defects. To achieve dynamic scheduling, we have proposed a Supervised and Distributed Holonic architecture we called SUDIHA. This architecture incorporates three Holons: Product Holon, Resource Holon and Order Holon and combines global supervision, achieved by Product Holon, with dynamic local control, achieved by Resource Holon. The Digital Twin (DT) concept is generally used to design CPS; it is virtual copies of the system that can interact with the physical counterparts in a bi-directional way. It seems to be promising to tackle the complexity and increase manufacturing system flexibility. In this paper, we use a DT Model to improve the SUDIHA architecture. We propose a Digital Twin based SUDIHA architecture (DT-SUDIHA). The paper will describe Digital Twins’ configuration of each Holon of the SUDIHA Architecture, and the intelligent and real time data driven operation control of this architecture. A case study is carried out at the ENSAM-Meknes flexible workshop to prove the effectiveness of the proposed approach.

MARL Sim2real Transfer: Merging Physical Reality With Digital Virtuality in Metaverse

Metaverse is an artificial virtual world mapped from and interacting with the real world. In metaverse, digital entities coexist with their physical counterparts. Powered by deep learning, metaverse is inevitably becoming more intelligent in the interactions between reality and virtuality. However, it is confronted with a nontrivial problem known as sim2real transfer when deep learning techniques try to bridge the reality gap between the physical world and simulations. In this article, we use multiagent deep reinforcement learning (MARL) to implement collective intelligence for digital entities as well as their physical counterparts. To model the immersive environments in metaverse, we define a nonstationary variant of Markov games and propose a recurrent MARL solution to it. Based on the solution, MARL sim2real transfer that bridges real and virtual multiple unmanned aerial vehicle (multi-UAV) systems is successfully conducted by employing recurrent multiagent deep deterministic policy gradient (R-MADDPG) with the domain randomization technique. Additionally, we use perception-control modularization to improve the generalization performance of MARL policies and make training more efficient.

Man as Image of Nature in Magnus Hundt: The Perspective of a Thomist ca. 1500

This paper draws on a late medieval example to show that images of nature can also be images of the human body. It presents the Anthropologium de hominis dignitate by the Leipzig magister Magnus Hundt (1449–1519). The Anthropologium is a text that prominently integrates the human body into its conception of man and its account of human dignity. The body is not presented as a prison of the soul, but as a perfectly balanced physical counterpart to the soul. The paper shows how Hundt’s reflections were influenced by his commitment to the Thomistic school. Moreover, it reveals how the elevated Imago Dei thesis provides a justification for the study of the human body. Hundt is shown to offer nothing less than a theological–philosophical legitimation for practising medicine. In doing so, he also incorporates images of nature in a literal sense, insofar as he includes images of the human body in his book.

Addressing time discrepancy between digital and physical twins

Digital twins (DTs) represent a key technology in the development, real-time monitoring and optimisation of cyber–physical systems (CPSs). Such potential emerges as a result of the real-time coupling between DTs and their physical counterparts, where it is possible to make use of operational data as it is being generated in order to aid decision-making. Harnessing this potential presents several design challenges, such as the parallel operation of the DT and its physical twin (PT), and the necessary synchronisation thereof, to ensure coherent execution of the system in ensemble. In this paper we present an approach that handles situations where a DT and its PT get out of sync as a result of disturbances in the normal operational conditions of the DT–PT system, e.g., due to network degradation or temporary network drop. The purpose is to provide a best-effort functionality covering: user notification, degradation of DT to digital shadow (DS), with recovery mechanisms to re-establish the synchronisation between DT and PT.

Developing a material flow monitor for the Netherlands from national statistical data

AbstractEffective monitoring of national circular economy policies requires consistent, national databases of material flows and environmental impacts. Yet, databases and indicators developed so far are scattered and inconsistent. To tackle this problem, Statistics Netherlands (CBS) developed a material flow monitor (MFM) that integrates existing statistics using principles of the System of Environmental Economic Accounting (SEEA). The MFM is the physical counterpart of the Dutch supply and use tables (SUT) of the National Accounts and is also referred to as physical SUT (P‐SUT). The P‐SUT captures national resource extraction, product imports and exports, product flows between economic sectors, as well as emissions and waste streams resulting from economic activities. Our work illustrates how a statistical office can use and enrich its formal statistical data to compose an MFM consistent with the National Accounts and how indicators can be extracted with a case study on the bio‐based economy. We contribute a clear step‐by‐step description of the method and the used datasets. This supports the development of MFMs by other statistical offices and researchers, thereby enabling consistent and comparable circular economy monitoring.

Digital Twins in Industry 4.0: A Literature Review

Digital Twin is one of the most promising fields in Industry 4.0 due to its advantages related to real-time monitoring, performance analysis, and predictive maintenance. It is a virtual up-to-date representation of a realworld asset, system, being, and even city that is updated in real-time with data from its physical counterpart. By bridging the physical and digital, it is considered to be the innovation backbone of the future. In this contribution, we review the concept of digital twins, the development of its uses in industrial applications, and the level of integration in scientific work.

On the Misappropriation of Spatial Metaphors in Online Learning

In online educational systems, teachers often replicate pedagogical methods, and online institutions replicate systems and structures used by their in-person counterparts, the only purpose of which was to solve problems created by having to teach in a physical environment. Likewise, virtual learning environments often attempt to replicate features of their physical counterparts, thereby weakly replicating in software the problems that in-person teachers had to solve. This has contributed to a vicious circle of problem creation and problem solving that benefits no one. In this paper I argue that the term ‘environment’ is a dangerously misleading metaphor for the online systems we build to support learning, that leads to poor pedagogical choices and weak digital solutions. I propose an alternative metaphor of infrastructure and services that can enable more flexible, learner-driven, and digitally native ways of designing systems (including the tools, pedagogies, and structures) to support learning.

Can we benefit from game engines to develop digital twins for planning the deployment of photovoltaics?

Digital Twins (DTs) have attracted great attention in the energy sector. Game engines have been suggested to model DTs of their physical counterparts because they provide realistic graphics, lighting-, fluid- and physics engines that simulate the real world. However, the application of game engines to develop DTs for photovoltaics (PVs) has not yet been discussed in the literature. Therefore, this paper assesses the built-in game engine features' ability to support the DT development of PVs with Unreal Engine 5. This paper mainly focuses on visual representation because the surrounding environment significantly impacts PV deployment, and the existing software tools do not allow the study of the environmental factors at the early planning phase of a project’s lifecycle. Furthermore, this paper investigates the position of the sun, shadows and reflections from nearby objects that influence the PVs' power output, and if the built-in light engine can be used for planning the deployment of PVs. The result shows that in-game objects in the environment can be used to affect the simulated PV output estimate over a year. It also indicates that applying Unreal Engine 5 to model PV systems that rely on mirroring real-world behaviour is promising if accurate data is used in the modelling. Real data and mathematical PV models are necessary since Unreal Engine 5’s Lumen subsystem cannot provide realistic solar radiance on PVs for a given location on earth.

“Kind of Mine, Kind of Not”: Digital Possessions and Affordance Misalignment

AbstractThe objects we consume increasingly exist in digital form, from audiobooks and digital photographs to social media profiles and avatars. Digital objects are often argued to be less valued, personally meaningful, and self-relevant than their physical counterparts and are consequently dismissed as poor candidates for possession. Yet, studies have identified highly meaningful, even irreplaceable, digital possessions. In this article, we account for these contradictory narratives surrounding digital possessions, arguing that digital objects are not inherently unsuited to possession, but rather their affordances may not align with consumers’ imagined affordances (i.e., the object affordances that consumers anticipate). Drawing from a qualitative study of 25 consumers and their digital possessions, we identify three recurring types of affordance misalignment—missing affordances, covert affordances, and deficient affordances—that mediate how consumers and digital objects interact (pragmatic mediation) and, consequently, consumers’ experiences of, and beliefs surrounding, digital objects as possessions (hermeneutic mediation). We demonstrate that these affordance misalignments can create obstacles to consumers’ desired experiences of possession and document consumers’ attempts to overcome these obstacles by employing alignment strategies, with varied behavioral outcomes. This article advances debates surrounding digital possessions and presents an enriched affordance theory lens that provides new insights into possession.

XPlaM: A toolkit for automating the acquisition of BDI agent-based Digital Twins of organizations

A Digital Twin ideally manifests the same behaviour (in silico) of its physical counterpart. While considerable attention has been paid to the development of Digital Twins for physical devices/systems, the question of developing Twins for organizations has received relatively little attention. The setting in which we address this problem is very general and, consequently, very challenging. We look at the automatic acquisition of Digital Twins of organizations. To that end, this paper builds on the following two premises: (1) that Digital Twins of organizations can provide value, and (2) that Belief-Desire-Intention (BDI) agents are a particularly effective means for representing Digital Twins. The overall approach is to leverage the externally observable behaviour of the target system and then generate candidate BDI agent programs that best explain (in the sense of formal abduction) the observed behaviour. The candidate agent programs are generated by searching through potentially large hypotheses spaces for possible plans, selection functions and beliefs. The resulting approach suggests that using abduction to generate Digital Twins of organizations in the form of BDI agents can be effective.