A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s present ...and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and object lifecycle management. As an emergent technology, its widespread implementation is increasing in several domains such as industrial, automotive, medicine, smart cities, etc. The objective of this systematic literature review is to present a comprehensive view on the DT technology and its implementation challenges and limits in the most relevant domains and applications in engineering and beyond.
The application of industrial technologies is undergoing significant changes. Finding the level at which to use efficient cyberphysical systems is perhaps one of the most important technical ...preparatory tasks in implementing digital manufacturing. Welding technology systems are investigated, and a framework for capturing the data sets required for data-driven manufacturing is developed. To make full autonomy in a manufacturing environment meaningful, formerly isolated groups of equipment need to be organized into a production information system. In our research, a test system is created that can implement a digital virtual interface and achieve new levels of efficiency with a future digital twin system. In the discourse of the study, the technological parameters of welding test pieces were investigated, namely the available measurement data sets of current, and voltage data. In the summary section, most of the tasks and research directions are presented, which can be envisaged as a continuation of the present study. Our study will be followed by further research, already testing a complete digital twin system, thus reaching another milestone on the way to autonomous manufacturing.
Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming ...aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.
A Digital Twin is a digital replica of a living or nonliving physical entity, and this emerging technology attracted extensive attention from different industries during the past decade. Although a ...few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, a mobility digital twin (MDT) framework is developed, which is defined as an artificial intelligence (AI)-based data-driven cloud-edge-device framework for mobility services. This MDT consists of three building blocks in the physical space (namely, Human , Vehicle , and Traffic ), and their associated Digital Twins in the digital space. An example cloud-edge architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to fulfill its digital functionalities of storage, modeling, learning, simulation, and prediction. A case study of the personalized adaptive cruise control (P-ACC) system is conducted, which integrates the key microservices of all three digital building blocks of the MDT framework: 1) the Human Digital Twin with user management and driver type classification; 2) the Vehicle Digital Twin with cloud-based advanced driver-assistance systems (ADAS); and 3) the Traffic Digital Twin with traffic flow monitoring and variable speed limit. Future challenges of the proposed MDT framework are discussed toward the end of the article, including standardization, AI for computing, public or private cloud service, and network heterogeneity.
•For the HPPs assembly workshops, a theoretical framework for the digital twin-driven assembly-commissioning is developed•For the digital twin assembly-commissioning process, an assembly ...comprehensive factor information model is established•The information exchange mechanism between the models is designed through knowledge-based interaction and interoperability interfaces•This paper establishes an assembly predictability model and a process optimization model
High precision products (HPPs) with multidisciplinary coupling are widely used in aerospace, marine, chemical and other fields. Since the internal structure of HPPs is complex and compact, the assembly process requires high precision and involves multidisciplinary coupling. Traditional assembly process of HPPs is based on manual experience, which results in low assembly efficiency and poor-quality consistency. Given the above problems, this research proposes a digital twin-driven assembly-commissioning approach for HPPs. Firstly, this paper introduces the theoretical framework of digital twin-driven assembly-commissioning. Secondly, we introduce the construction method of assembly-commissioning total factor information model based on digital twin technology; the fusion method of twin data and the interoperability method between digital twin models; in addition, the assembliability prediction and assembly-commissioning process optimization methods. Finally, a case study product is used to verify the effectiveness and feasibility of the proposed method.
Abstract
The report is concerned with the analysis of the functional and physical structures of human characteristics for the formation of reference digital models and individual portraits of a human ...which are his digital twins. A database structure has been developed for describing the human state, based on a variety of models, measured physical quantities, and results obtained from a mobile information and measurement system. The paper outlines an approach to the study and design of mobile information and measurement system designed for the construction of individual kinematic portraits of a human. A variant of a part of the inference system is shown on the example of studying the trend of changes in one of the time characteristics of the movement technique.
•An image segmentation model is built on Memory-augmented Neural Networks (MANNs), in an effort to identify the ever-boosting image information with more details.•Results demonstrate that the ...MANNs-based image segmentation model is more accurate and•consumes less training time than other classic models.
With the continuous increase of the amount of information, people urgently need to identify the information in the image in more detail in order to obtain richer information from the image. This work explores the dynamic complex image segmentation of self-driving vehicle under Digital Twins (DTs) based on Memory-augmented Neural Networks (MANNs), so as to further improve the performance of self-driving in intelligent transportation. In view of the complexity of the environment and the dynamic changes of the scene in intelligent transportation, this work constructs a segmentation model for dynamic complex image of self-driving vehicle under DTs based on MANNs by optimizing the Deep Learning algorithm and further combining with the DTs technology, so as to recognize the information in the environment image during the self-driving. Finally, the performance of the constructed model is analyzed by experimenting with different image datasets (PASCALVOC 2012, NYUDv2, PASCAL CONTEXT, and real self-driving complex traffic image data). The results show that compared with other classical algorithms, the established MANN-based model has an accuracy of about 85.80%, the training time is shortened to 107.00 s, the test time is 0.70 s, and the speedup ratio is high. In addition, the average algorithm parameter of the given energy function α=0.06 reaches the maximum value. Therefore, it is found that the proposed model shows high accuracy and short training time, which can provide experimental reference for future image visual computing and intelligent information processing.
AbstractTo meet energy-reduction goals, cities are challenged with assessing building energy performance and prioritizing efficiency upgrades across existing buildings. Although current top-down ...building energy benchmarking approaches are useful for identifying overall efficient and poor performers across a portfolio of buildings at a city scale, they are limited in their ability to provide actionable insights regarding efficiency opportunities. Concurrently, advances in smart metering data analytics combined with new data streams available via smart metering infrastructure present the opportunity to incorporate previously undetectable temporal fluctuations into top-down building benchmarking analyses. This paper leveraged smart meter electricity data to develop daily building energy benchmarks segmented by strategic periods to quantify their variation from conventional, annual energy benchmarking strategies and investigate how such metrics can lead to near real-time energy management. The periods considered include occupied periods during the school year, unoccupied periods during the school year, occupied periods during the summer, unoccupied periods during the summer, and peak summer demand periods. Results showed that temporally segmented building energy benchmarks are distinct from a building’s overall benchmark. This demonstrates that a building’s overall benchmark masks periods in which a building is over- or underperforming during the day, week, or month; thus, temporally segmented energy benchmarks can provide a more specific and accurate measure for building efficiency. We discussed how these findings establish the foundation for digital twin–enabled urban energy management platforms by enabling identification of building retrofit strategies and near-real-time efficiency in the context of the performance of an entire building portfolio. Temporally segmented energy benchmarking measures generated from smart meter data streams are a critical step for integrating smart meter analytics with building energy benchmarking techniques, and for conducting smarter energy management across a large geographic scale of buildings.
The development of Internet of Vehicles (IoV) has produced a considerable amount of real-time traffic data. These traffic data constitute a kind of digital twin that connects the physical vehicles ...and their virtual representation via 5G communications. Generally, through analyzing the digital twin traffic data, traffic administrators can optimize traffic scheduling and alleviate traffic jams. However, the exceptions of IoV sensors inevitably raise an issue of traffic data sparsity and consequently influence scientific traffic scheduling decisions. Inspired by this drawback, in this article, a digital twin-assisted real-time traffic data prediction method is proposed by analyzing the traffic flow and velocity data monitored by IoV sensors and transmitted through 5G. At last, we conduct a set of experiments based on a traffic dataset collected by Nanjing city of China. Reported results show the feasibility of our proposal in smart traffic flow and velocity prediction that call for a quick response and high accuracy.
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