scholarly journals Adventures in Winter Wonderland - observing user behaviour in a digital twin bookstore

2020 ◽  
Author(s):  
Elke Greifeneder ◽  
◽  
Maria Gäde ◽  
Keyword(s):  
Visual Information ◽  
Observation Data ◽  
Web Analytics ◽  
User Behaviour ◽  
Digital Twin ◽  
Digital Twins ◽  
Online Behaviour ◽  
Physical Spaces ◽  
Casual Leisure ◽  
Insight Into

Introduction. In casual leisure seeking contexts, visual information plays an important role for exploration or even serendipity affects. However, existing systems rarely support alternative search or browsing strategies. In the case study of this paper, we propose an approach to evaluate the potential and challenges of digital twins as digital replica of physical spaces. Method. A mixed-method approach is applied combining observation data in the physical twin with web analytics in the digital twin. Analysis. Both quantitative and qualitative analyses were applied to the data sources and comparisons between user behaviour in the physical and digital place are drawn. Results. We report preliminary results and discuss the opportunities and limitations of current practices to observe digital and physical spaces. The data analyses have given a broad insight into behaviours in the physical twin. However, standard web analytics approaches could not reveal the same insight into online behaviour, showing the limitations of current research practices. Conclusions. The analysis of digital twins requires the combination of offline and online practices. Experiences from the physical observation can inform the analysis of the digital space. It seems to be clear, that the analysis of log data alone is not sufficient but needs to be completed by other user behaviour methods.

Smart Connected Digital Products and IoT Platform With the Digital Twin

2021 ◽  
pp. 330-350
Author(s):  
Mohamed Uvaze Ahamed Ayoobkhan ◽  
Yuvaraj D. ◽  
Jayanthiladevi A. ◽  
Balamurugan Easwaran ◽  
ThamaraiSelvi R.
Keyword(s):  
Associate Degree ◽  
Physical Object ◽  
Digital Products ◽  
Object A ◽  
Digital Twin ◽  
Physical Product ◽  
Digital Twins ◽  
And Performance ◽  
And Behavior ◽  
Insight Into

A digital illustration of a novel prevalence of a physical product helps one to gain larger insight into that product's state performance and behavior digital twin, which is an unequivocal advanced copy of an item, method, or control. This living model creates a thread between the physical and digital worlds. A model of a physical object—a 'twin'—enables you to observe its standing, diagnose problems, and take a look at solutions remotely. It's a dynamic virtual illustration of a tool that is unendingly fed with knowledge from embedded sensors and packages. This provides associate degree correct period of time standing of the physical device. Digital twins drive innovation and performance and offer development technicians prognostic analytics that give firms the flexibility to boost client expertise.

scholarly journals Maritime Digital Twin architecture

2021 ◽  
Vol 69 (12) ◽  
pp. 1081-1095
Author(s):  
Jan-Erik Giering ◽  
Alexander Dyck
Keyword(s):  
Use Cases ◽  
Maritime Industry ◽  
Product Lifecycle ◽  
Political Pressure ◽  
Digital Twin ◽  
Current State ◽  
Delivery Costs ◽  
Digital Twins ◽  
Insight Into

Abstract Digital Twins (DTs) play an important role in current digitalization trends across industries. As maritime markets are particularly affected by recent global tendencies such as increasing delivery costs or political pressure for decarbonization, DT solutions could provide important support for shipbuilding and shipping companies to master recent and upcoming challenges. This paper provides a brief insight into the current state of the maritime industry and shows possible use-cases for DT Ship applications throughout the entire product lifecycle. To further advance the general understanding of DTs and their implementation, the concept of a Maritime Digital Twin Architecture (MDTA) is proposed to structure practical DT features.

Digital Twin for Smart Manufacturing: The Practitioner’s Perspective

2020 ◽  
Author(s):  
Maja Bärring ◽  
Björn Johansson ◽  
Goudong Shao
Keyword(s):  
Conceptual Development ◽  
Production Systems ◽  
Manufacturing Sector ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Adoption And Implementation ◽  
New Information ◽  
Digital Twins ◽  
Discrete Manufacturing ◽  
Design Production

Abstract The manufacturing sector is experiencing a technological paradigm shift, where new information technology (IT) concepts can help digitize product design, production systems, and manufacturing processes. One of such concepts is Digital Twin and researchers have made some advancement on both its conceptual development and technological implementations. However, in practice, there are many different definitions of the digital-twin concept. These different definitions have created a lot of confusion for practitioners, especially small- and medium-sized enterprises (SMEs). Therefore, the adoption and implementation of the digital-twin concept in manufacturing have been difficult and slow. In this paper, we report our findings from a survey of companies (both large and small) regarding their understanding and acceptance of the digital-twin concept. Five supply-chain companies from discrete manufacturing and one trade organization representing suppliers in the automotive business were interviewed. Their operations have been studied to understand their current digital maturity levels and articulate their needs for digital solutions to stay competitive. This paper presents the results of the research including the viewpoints of these companies in terms of opportunities and challenges for implementing digital twins.

Machine learning-based digital twins reduce seasonal remapping in aeroderivative gas turbines

2021 ◽  
pp. 1-7
Author(s):  
Nick Petro ◽  
Felipe Lopez
Keyword(s):  
Machine Learning ◽  
Ambient Temperature ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Pilot Site ◽  
Digital Twin ◽  
Digital Twins ◽  
Machine Learning Model ◽  
Unacceptable Behavior ◽  
External Conditions

Abstract Aeroderivative gas turbines have their combustion set points adjusted periodically in a process known as remapping. Even turbines that perform well after remapping may produce unacceptable behavior when external conditions change. This article introduces a digital twin that uses real-time measurements of combustor acoustics and emissions in a machine learning model that tracks recent operating conditions. The digital twin is leveraged by an optimizer that select adjustments that allow the unit to maintain combustor dynamics and emissions in compliance without seasonal remapping. Results from a pilot site demonstrate that the proposed approach can allow a GE LM6000PD unit to operate for ten months without seasonal remapping while adjusting to changes in ambient temperature (4 - 38 °C) and to different fuel compositions.

Digital Twins: Review and Challenges

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Maria G. Juarez ◽  
Vicente J. Botti ◽  
Adriana S. Giret
Keyword(s):  
Computer Science ◽  
Industry 4.0 ◽  
Multi Agent System ◽  
Agent System ◽  
Digital Twin ◽  
Digital Twins ◽  
Science Approach ◽  
Multi Agent ◽  
Current Operating

Abstract With the arises of Industry 4.0, numerous concepts have emerged; one of the main concepts is the digital twin (DT). DT is being widely used nowadays, however, as there are several uses in the existing literature; the understanding of the concept and its functioning can be diffuse. The main goal of this paper is to provide a review of the existing literature to clarify the concept, operation, and main characteristics of DT, to introduce the most current operating, communication, and usage trends related to this technology, and to present the performance of the synergy between DT and multi-agent system (MAS) technologies through a computer science approach.

The Digital Twin of a System: A Structure for Networks of Digital Twins

2021 ◽  
Author(s):  
Leif- Thore Reiche ◽  
Claas Steffen Gundlach ◽  
Gian Frederik Mewes ◽  
Alexander Fay
Keyword(s):  
Digital Twin ◽  
System A ◽  
Digital Twins

Engine Fleet-Management: The Use of Digital Twins From a MRO Perspective

2017 ◽  
Author(s):  
Joern Kraft ◽  
Stefan Kuntzagk
Keyword(s):  
Multilevel Models ◽  
Operating Cost ◽  
Maintenance Cost ◽  
Fleet Management ◽  
Component Level ◽  
The Real ◽  
Digital Twin ◽  
Digital Twins ◽  
The Individual ◽  
Life Consumption

Engine operating cost is a major contributor to the direct operating cost of aircraft. Therefore, the minimization of engine operating cost per flight-hour is a key aspect for airlines to operate successfully under challenging market conditions. The interaction between maintenance cost, operating cost, asset value, lease and replacement cost describes the area of conflict in which engine fleets can be optimized. State-of-the-art fleet management is based on advanced diagnostic and prognostic methods on engine and component level to provide optimized long-term removal and work-scoping forecasts on fleet level based on the individual operation. The key element of these methods is a digital twin of the active engines consisting of multilevel models of the engine and its components. This digital twin can be used to support deterioration and failure analysis, predict life consumption of critical parts and relate the specific operation of a customer to the real and expected condition of the engines on-wing and at induction to the shop. The fleet management data is constantly updated based on operational data sent from the engines as well as line maintenance and shop data. The approach is illustrated along the real application on the CFM56-5C, a mature commercial two-spool high bypass engine installed on the Airbus A340-300. It can be shown, that the new methodology results in major improvements on the considered fleets.

scholarly journals Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning

2021 ◽  
Vol 6 (11) ◽  
pp. 157
Author(s):  
Gonçalo Pereira ◽  
Manuel Parente ◽  
João Moutinho ◽  
Manuel Sampaio
Keyword(s):  
Machine Learning ◽  
Fuel Consumption ◽  
Great Variability ◽  
Accurate Estimation ◽  
Estimation Model ◽  
Digital Twins ◽  
Significant Step ◽  
The Cost ◽  
Consumption Prediction ◽  
Insight Into

Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have a great variability of fuel consumption depending on the scenario of utilization. This paper describes the creation of a framework aiming to estimate the fuel consumption of construction trucks depending on the carried load, the slope, the distance, and the pavement type. Having a more accurate estimation will increase the benefit of these optimization tools. The fuel consumption estimation model was developed using Machine Learning (ML) algorithms supported by data, which were gathered through several sensors, in a specially designed datalogger with wireless communication and opportunistic synchronization, in a real context experiment. The results demonstrated the viability of the method, providing important insight into the advantages associated with the combination of sensorization and the machine learning models in a real-world construction setting. Ultimately, this study comprises a significant step towards the achievement of IoT implementation from a Construction 4.0 viewpoint, especially when considering its potential for real-time and digital twins applications.

scholarly journals Development and application of problem-oriented digital twins for magnetic observatories and variation stations

2021 ◽  
pp. 60-71
Author(s):  
Andrei Vorobev ◽  
Vyacheslav Pilipenko ◽  
Gulnara Vorobeva ◽  
Olga Khristodulo
Keyword(s):  
Time Series ◽  
Geomagnetic Field ◽  
Missing Values ◽  
Information Environment ◽  
Small Scale ◽  
Geomagnetic Variation ◽  
Digital Twin ◽  
Digital Twins ◽  
Geomagnetic Data ◽  
Magnetic Station

Introduction: Magnetic stations are one of the main tools for observing the geomagnetic field. However, gaps and anomalies in time series of geomagnetic data, which often exceed 30% of the number of recorded values, negatively affect the effectiveness of the implemented approach and complicate the application of mathematical tools which require that the information signal is continuous. Besides, the missing values ​​add extra uncertainty in computer simulation of dynamic spatial distribution of geomagnetic variations and related parameters. Purpose: To develop a methodology for improving the efficiency of technical means for observing the geomagnetic field. Method: Creation of problem-oriented digital twins of magnetic stations, and their integration into the collection and preprocessing of geomagnetic data, in order to simulate the functioning of their physical prototypes with a certain accuracy. Results: Using Kilpisjärvi magnetic station (Finland) as an example, it is shown that the use of digital twins, whose information environment is made up of geomagnetic data from adjacent stations, can provide the opportunity for reconstruction (retrospective forecast) of geomagnetic variation parameters with a mean square error in the auroral zone of up to 11.5 nT. The integration of problem-oriented digital twins of magnetic stations into the processes of collecting and registering geomagnetic data can provide automatic identification and replacement of missing and abnormal values, increasing, due to the redundancy effect, the fault tolerance of the magnetic station as a data source object. For example, the digital twin of Kilpisjärvi station recovers 99.55% of annual information, and 86.73% of it has an error not exceeding 12 nT. Discussion: Due to the spatial anisotropy of geomagnetic field parameters, the error at the digital twin output will be different in each specific case, depending on the geographic location of the magnetic station, as well as on the number of the surrounding magnetic stations and the distance to them. However, this problem can be minimized by integrating geomagnetic data from satellites into the information environment of the digital twin. Practical relevance: The proposed methodology provides the opportunity for automated diagnostics of time series of geomagnetic data for outliers and anomalies, as well as restoration of missing values and identification of small-scale disturbances.

scholarly journals Reconstructing feedback representations in ventral visual pathway with a generative adversarial autoencoder

2020 ◽  
Author(s):  
Haider Al-Tahan ◽  
Yalda Mohsenzadeh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Visual Pathway ◽  
Functional Magnetic Resonance ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
High Level ◽  
Feedback Connections ◽  
The Brain ◽  
Insight Into

AbstractWhile vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.Author summaryIt has been shown that the ventral visual cortex consists of a dense network of regions with feedforward and feedback connections. The feedforward path processes visual inputs along a hierarchy of cortical areas that starts in early visual cortex (an area tuned to low level features e.g. edges/corners) and ends in inferior temporal cortex (an area that responds to higher level categorical contents e.g. faces/objects). Alternatively, the feedback connections modulate neuronal responses in this hierarchy by broadcasting information from higher to lower areas. In recent years, deep neural network models which are trained on object recognition tasks achieved human-level performance and showed similar activation patterns to the visual brain. In this work, we developed a generative neural network model that consists of encoding and decoding sub-networks. By comparing this computational model with the human brain temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) response patterns, we found that the encoder processes resemble the brain feedforward processing dynamics and the decoder shares similarity with the brain feedback processing dynamics. These results provide an algorithmic insight into the spatiotemporal dynamics of feedforward and feedback processes in biological vision.

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