The rapid development in underground infrastructure is encouraging faster and more modern ways, such as TBM tunneling, to meet the needs of the world. However, tunneling activities generate complex and heterogeneous data, which makes it difficult to visualize the performance of a project. Advancements in information
Summary: Digital Twins is a concept based in IoT but requiring the skills of machine learning and potentially AI. It''s not completely new but it is integral to Gartner''s vision of the digital enterprise and makes the Hype Cycle for 2017.
1. Introduction The digital twin is often defined as the creation of a digitalized and comprehensive representation of a physical system, service, or product that includes valuable information gained throughout all of its lifecycle phases [1, 2].Once employed, a digital
Digital twins, Internet of Things (IoT), block chains, and Artificial Intelligence (AI) may redefine our imagination and future vision of globalization. Digital Twin will likely affect most of the
The massive growth in data-centers has led to increased interest and regulations for management of waste heat and its utilization. This work seeks to develop a combined Digital-Twin and Machine-Learning framework to optimize such systems by controlling both the ventilation and the cooling of the bases of data units/processors in
A digital twin system was also built for CLHG process based on the prediction results. Fig. 2 provides a comprehensive schematic of our process for predicting the reactivity of OCs through machine learning. Download : Download high-res image (899KB) Download : Download full-size image; Fig. 2. Flowchart of machine learning
Accordingly, the Digital-Twin and Machine-Learning in this framework combines these rapidly computable models with genomic Machine-Learning methods to ascertain the placement and flow rates of multiple mobile ventilation units, in order to optimally sequester particles released from respiratory emissions such as coughs,
This work develops a computational Digital-Twin framework to track and optimize the flow of solar power through complex, multipurpose, solar farm facilities, such as Agrophotovoltaic (APV) systems. APV systems symbiotically cohabitate power-generation facilities and agricultural production systems. In this work, solar power flow is rapidly
Digital twins use machine learning, data analytics, and multi-physics simulation in order simulate and analyze different working conditions and other factors affect a system [4]. The creation of the digital twin is a critical component of future technology that will have an impact on several global sectors [5] .
Two key technologies that have emerged within this transformation are digital twins (DTs) and machine learning (ML). A DT is defined as "a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making" [ 2 ].
The objective of this work is to present a new approach to create a machine learning-based digital twin of banana fruit to monitor its quality changes throughout storage. The thermal camera has been used as a data acquisition tool due to its capability to detect the surface and physiological changes of fruits throughout the storage. In this
The biggest difference between virtual twins and machine-powered learning. The main aspect that differentiates these technologies is that Machine Learning works on gathering its initial data from distinctions. Meaning, that the technology begins its work and "starts thinking" by itself once an objective has been set and accurately
train the digital twin, which is a machine learning clas sifier, see Fig. 7. The trained classifier can be used in the digital t win platform to deploy a diagnosis
Fig. 1 shows a schematic representation of a digital twin conceptual framework. Measurements are taken from the physical twin (wind turbine) to calibrate/update the digital twin. The digital twin is composed of a computational model (physics-based and/or machine learning models) and a stochastic layer to take into
A digital-twin and machine-learning framework for precise heat and energy management of data-centers: a. A small number of studies published in 2023 are indexed with the publication year 2022. Recommended articles. Data availability. Data will be made available on request. References [1]
Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable informa
Considering that, this study shows the result of digital twins (DT) and machine learning (ML) algorithms for fault classification used as PdM tools in a thermoelectric complex, supported by an architecture integrated with the supervisory system of the industry. Machine learning based digital twin framework for production
Machine learning (ML) is an AI technique that develops statistical models and algorithms so that computer systems perform tasks without explicit instructions, relying on patterns and inference instead. Digital twin technology uses machine learning algorithms to process the large quantities of sensor data and identify data patterns.
The basic architecture of digital twin consists of the sensor and measurement technologies, Internet of Things, and machine learning. From the computational perspective, the key technology to propel a digital twin is the data and information fusion that facilitates the flow of information from raw sensory data to high
It uses real-world data, simulation or machine learning models, combined with data analysis, to enable understanding, learning, and reasoning. Digital twins can be used to answer what-if questions and should be able to present the insights in an intuitive way." Digital twins have one fundamental purpose: To model the behavior of
The contributions of the present paper are twofold: (i) to construct a digital twin conceptual framework, in the context of structural dynamics, to detect damage in
Easily model and create digital representations of connected environments with an open modeling language. Model buildings, factories, farms, energy networks, railways, stadiums—even entire cities. Bring these digital twins to life with a live execution environment that historizes twin changes over time. Unlock actionable insights into the
Machine learning and digital twin improve one another. The first benefit of a digital twin is the ability to produce simulated data. A virtual environment can go through an infinite number of repetitions and scenarios. The simulated data produced can then be used to train the AI model. This way the AI system can be taught potential real-world
Progress in Big Data, Earth system science (ESS), machine learning and cyberinfrastructure is enabling rapid advancement toward Earth''s digital twin 17,19,20.
Digital twin examples Digital twin technology is expected to grow exponentially in the near future due largely to the increased adoption of advanced technologies such as AI, machine learning, the Internet of Things (IoT), extended reality, and 5G networking . It has already been adopted in many industries, which employ it in
Digital twins use machine learning, data analytics, and multi-physics simulation in order simulate and analyze different working conditions and other factors affect a system [4]. The creation of the digital twin is a critical component of future technology that will have an impact on several global sectors [5].
This paper presents a framework for integrating Explainable and Anomalous Machine Learning (EAML) into a digital twin to enable finetuning of mixtures as a mean to realize next-gen concretes with favorable performance. In this framework, both anomalous unsupervised and explainable supervised ML algorithms are joined to create a virtual