Technological advancements of urban informatics and vehicular intelligence have enabled connected smart vehicles as pervasive edge computing platforms for a plethora of powerful applications. ...However, varies types of smart vehicles with distinct capacities, diverse applications with different resource demands as well as unpredictive vehicular topology, pose significant challenges on realizing efficient edge computing services. To cope with these challenges, we incorporate digital twin technology and artificial intelligence into the design of a vehicular edge computing network. It centrally exploits potential edge service matching through evaluating cooperation gains in a mirrored edge computing system, while distributively scheduling computation task offloading and edge resource allocation in an multiagent deep reinforcement learning approach. We further propose a coordination graph driven vehicular task offloading scheme, which minimizes offloading costs through efficiently integrating service matching exploitation and intelligent offloading scheduling in both digital twin and physical networks. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.
The rapid development of industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower ...the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing efficiency. In this article, we first propose a new paradigm digital twin network to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present asynchronous actor-critic algorithm to find the optimal stochastic computation offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.
The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the ...limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.
Abstract
With the development of science and technology, modern enterprise equipment is more and more complex and widely distributed. The requirement of “accuracy” of equipment maintenance management ...is higher and higher. Accurate equipment maintenance management is of great significance for accurate support of modern enterprise. By analyzing the current problems faced by modern enterprise equipment maintenance management, the digital twin technology is introduced into the field of equipment maintenance management, and the idea of modern enterprise equipment maintenance managemen based on digital twin is proposed. From the physical layer, twin layer, application layer and connection layer, the equipment model based on digital twin is established, the operation of the model is analyzed, and the application method of the model is prospected.
Abstract
One method for finding reliable and cost-effective solutions for designing radioisotope production systems is represented by the “digital twin” philosophy of design. Looking at cyclotron ...solid targets, uncertainties of the particle beam, material composition and geometry play a crucial role in determining the results. The difference between what has been designed and what can be effectively manufactured, where processes such as electroplating are poorly controllable and generate large non-uniformities in deposition, must also be considered. A digital twin, where the target geometry is 3D scanned from real models, can represent a good compromise for connecting “ideal” and “real” worlds. Looking at the
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Ni(p,n)
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Cu reaction, different Unstructured-Mesh MCNP6 models have been built starting from the 3D solid target system designed and put into operation by COMECER. A characterization has been performed considering the designed ideal target and a 3D scan of a real manufactured target measured with a ZEISS contact probe. Libraries and physics models have been also tested due to limited cross-section data. Proton spectra in the target volume, 3D proton-neutron-photon flux maps, average energies, power to be dissipated, shut-down dose-rate,
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Cu yield compared with various sources of experimental data and beam axial shifting impact, have been estimated. A digital twin of the
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Ni(p,n)
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Cu production device has been characterized, considering the real measured target geometry, paving the way for a fully integrated model suitable also for thermal, structural or fluid-dynamic analyses.
In this paper, we consider a mobile edge computing system with both ultra-reliable and low-latency communications services and delay tolerant services. We aim to minimize the normalized energy ...consumption, defined as the energy consumption per bit, by optimizing user association, resource allocation, and offloading probabilities subject to the quality-of-service requirements. The user association is managed by the mobility management entity (MME), while resource allocation and offloading probabilities are determined by each access point (AP). We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server. From the pre-trained deep neural network (DNN), the MME can obtain user association scheme in a real-time manner. Considering that the real networks are not static, the digital twin monitors the variation of real networks and updates the DNN accordingly. For a given user association scheme, we propose an optimization algorithm to find the optimal resource allocation and offloading probabilities at each AP. The simulation results show that our method can achieve lower normalized energy consumption with less computation complexity compared with an existing method and approach to the performance of the global optimal solution.
Summary
This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven physics‐based digital twins. ...Reduced‐order modeling produces physics‐based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component‐based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation—both critical features in the digital twin context. Data‐driven model adaptation and uncertainty quantification are formulated as a Bayesian state estimation problem, in which sensor data are used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12‐ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics‐based digital twin of the aircraft structure. The data‐driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation.
The rapid development of digitalization, the Internet of Things (IoT), and Industry 4.0 has led to the emergence of the digital twin concept. IoT is an important pillar of the digital twin. The ...digital twin serves as a crucial link, merging the physical and digital territories of Industry 4.0. Digital twins are beneficial to numerous industries, providing the capability to perform advanced analytics, create detailed simulations, and facilitate informed decision-making that IoT supports. This paper presents a review of the literature on digital twins, discussing its concepts, definitions, frameworks, application methods, and challenges. The review spans various domains, including manufacturing, energy, agriculture, maintenance, construction, transportation, and smart cities in Industry 4.0. The present study suggests that the terminology “3 dimensional (3D) digital twin” is a more fitting descriptor for digital twin technology assisted by IoT. The aforementioned statement serves as the central argument of the study. This article advocates for a shift in terminology, replacing “digital twin” with “3D digital twin” to more accurately depict the technology’s innate potential and capabilities in Industry 4.0. We aim to establish that “3D digital twin” offers a more precise and holistic representation of the technology. By doing so, we underline the digital twin’s analytical ability and capacity to offer an intuitive understanding of systems, which can significantly streamline decision-making processes using the digital twin.
The waste electrical and electronic equipment (WEEE) recovery can be categorised into two types, i.e. recycling at the material level and remanufacturing at the component level. However, the WEEE ...recovery is facing enormous challenges of diversified individuals, lack of product knowledge, distributed location, and so forth. On the other hand, the latest ICT provides new methods and opportunities for industrial operation and management. Thus, in this research digital twin and Industry 4.0 enablers are introduced to the WEEE remanufacturing industry. The goal is to provide an integrated and reliable cyber-avatar of the individual WEEE, thus forming personalised service system. The main contribution presented in this paper is the novel digital twin-based system for the WEEE recovery to support the manufacturing/remanufacturing operations throughout the product's life cycle, from design to recovery. Meanwhile, the international standard-compliant data models are also developed to support WEEE recovery services with high data interoperability. The feasibility of the proposed system and methodologies is validated and evaluated during implementations in the cloud and cyber-physical system.
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