A Digital Twin Method for Automated Behavior Analysis of Large-Scale Distributed IoT Systems

2019 ◽  
Author(s):  
Jack Sleuters ◽  
Yonghui Li ◽  
Jacques Verriet ◽  
Marina Velikova ◽  
Richard Doornbos
Keyword(s):  
Behavior Analysis ◽  
Large Scale ◽  
Digital Twin ◽  
Twin Method

scholarly journals Understanding design change propagation in complex engineering systems using a digital twin and design structure matrix

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Long Chen ◽  
Jennifer Whyte
Keyword(s):  
Large Scale ◽  
Load Capacity ◽  
Prediction Method ◽  
Change Propagation ◽  
Engineering Systems ◽  
Content Type ◽  
Design Change ◽  
Digital Twin ◽  
Complex Engineering ◽  
Complex Engineering Systems

PurposeAs the engineering design process becomes increasingly complex, multidisciplinary teams need to work together, integrating diverse expertise across a range of disciplinary models. Where changes arise, these design teams often find it difficult to handle these design changes due to the complexity and interdependencies inherent in engineering systems. This paper aims to develop an innovative approach to clarifying system interdependencies and predicting the design change propagation at the asset level in complex engineering systems based on the digital-twin-driven design structure matrix (DSM).Design/methodology/approachThe paper first defines the digital-twin-driven DSM in terms of elements and interdependencies, where the authors have defined three types of interdependency, namely, geospatial, physical and logical, at the asset level. The digital twin model was then used to generate the large-scale DSMs of complex engineering systems. The cluster analysis was further conducted based on the improved Idicula–Gutierrez–Thebeau algorithm (IGTA-Plus) to decompose such DSMs into modules for the convenience and efficiency of predicting design change propagation. Finally, a design change propagation prediction method based on the digital-twin-driven DSM has been developed by integrating the change prediction method (CPM), a load-capacity model and fuzzy linguistics. A section of an infrastructure mega-project in London was selected as a case study to illustrate and validate the developed approach.FindingsThe digital-twin-driven DSM has been formally defined by the spatial algebra and Industry Foundation Classes (IFC) schema. Based on the definitions, an innovative approach has been further developed to (1) automatically generate a digital-twin-driven DSM through the use of IFC files, (2) to decompose these large-scale DSMs into modules through the use of IGTA-Plus and (3) predict the design change propagation by integrating a digital-twin-driven DSM, CPM, a load-capacity model and fuzzy linguistics. From the case study, the results showed that the developed approach can help designers to predict and manage design changes quantitatively and conveniently.Originality/valueThis research contributes to a new perspective of the DSM and digital twin for design change management and can be beneficial to assist designers in making reasonable decisions when changing the designs of complex engineering systems.

scholarly journals Digital Twins-Based Smart Design and Control of Ultra-Precision Machining: A Review

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1717
Author(s):  
Lei Wu ◽  
Jiewu Leng ◽  
Bingfeng Ju
Keyword(s):  
Information Asymmetry ◽  
Large Scale ◽  
Planning Process ◽  
Design Stage ◽  
Precision Machining ◽  
Digital Twin ◽  
Module Design ◽  
Digital Twins ◽  
Ultra Precision ◽  
And Control

Ultra-Precision Machining (UPM) is a kind of highly accurate processing technology developed to satisfy the manufacturing requirements of high-end cutting-edge products including nuclear energy producers, very large-scale integrated circuits, lasers, and aircraft. The information asymmetry phenomenon widely exists in the design and control of ultra-precision machining. It may lead to inconsistency between the designed performance and operational performance of the UPM equipment on stiffness, thermal stability, and motion accuracy, which result from its design, manufacturing, and control, and determine the form accuracy and surface roughness of machined parts. The performance of the UPM equipment should be improved continuously. It is still challenging to realize the real-time and self-adaptive control, in which building a high-fidelity and computationally efficient digital twin is a valuable solution. Nevertheless, the incorporation of the digital twin technology into the UPM design and control remains vague and sometimes contradictory. Based on a literature search in the Google Scholar database, the critical issues in the UPM design and control, and how to use the digital twin technologies to promote it, are reviewed. Firstly, the digital twins-based UPM design, including bearings module design, spindle-drive module design, stage system module design, servo module design, and clamping module design, are reviewed. Secondly, the digital twins-based UPM control studies, including voxel modeling, process planning, process monitoring, vibration control, and quality prediction, are reviewed. The key enabling technologies and research directions of digital twins-based design and control are discussed to deal with the information asymmetry phenomenon in UPM.

scholarly journals Unsupervised online multitask learning of behavioral sentence embeddings

2019 ◽  
Vol 5 ◽  
pp. e200
Author(s):  
Shao-Yen Tseng ◽  
Brian Baucom ◽  
Panayiotis Georgiou
Keyword(s):  
Emotion Recognition ◽  
Behavior Analysis ◽  
Couples Therapy ◽  
Large Scale ◽  
State Of The Art ◽  
General Purpose ◽  
Multitask Learning ◽  
Multiple Tasks ◽  
And Behavior ◽  
Better Than

Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora. Recent research, however, has shown that sentence embeddings trained using in-domain data or supervised techniques, often through multitask learning, perform better than unsupervised ones. Representations have also been shown to be applicable in multiple tasks, especially when training incorporates multiple information sources. In this work we aspire to combine the simplicity of using abundant unsupervised data with transfer learning by introducing an online multitask objective. We present a multitask paradigm for unsupervised learning of sentence embeddings which simultaneously addresses domain adaption. We show that embeddings generated through this process increase performance in subsequent domain-relevant tasks. We evaluate on the affective tasks of emotion recognition and behavior analysis and compare our results with state-of-the-art general-purpose supervised sentence embeddings. Our unsupervised sentence embeddings outperform the alternative universal embeddings in both identifying behaviors within couples therapy and in emotion recognition.

A fiber optics enriched Digital Twin for assessment of reinforced concrete structures

2021 ◽  
Author(s):  
Carlos G. Berrocal ◽  
Ignasi Fernandez ◽  
Rasmus Rempling
Keyword(s):  
Reinforced Concrete ◽  
Fiber Optics ◽  
Asset Management ◽  
Web Application ◽  
Large Scale ◽  
Management Strategies ◽  
Reinforced Concrete Beam ◽  
Structural Condition ◽  
Ongoing Research ◽  
Digital Twin

<p>This paper presents the results of <i>SensIT</i>, an ongoing research initiative at Chalmers University of Technology aimed at developing a digital twin concept to improve the asset management strategies of reinforced concrete infrastructure. The developed concept relies on data collected from distributed optical fiber sensors (DOFS), which are then analysed to extract relevant features, such as deflections and crack widths, that can be used as indicators of the structural performance. Thereafter, intuitive contour plots are generated to deliver critical information about the element’s structural condition in a clear and straightforward manner. Last, both raw and analysed data are integrated into a collaborative web application where information can be readily accessed, and results can be visualized directly onto a 3D model of the element. The concept has been tested on a large-scale reinforced concrete beam subjected to flexural loading in laboratory conditions.</p>

scholarly journals Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network

2020 ◽  
Vol 10 (6) ◽  
pp. 1908
Author(s):  
Ju-Chin Chen ◽  
Chien-Yi Lee ◽  
Peng-Yu Huang ◽  
Cheng-Rong Lin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Behavior Analysis ◽  
Large Scale ◽  
Traffic Accidents ◽  
Driver Behavior ◽  
World Health ◽  
Status Report ◽  
The World ◽  
Level Fusion

According to the World Health Organization global status report on road safety, traffic accidents are the eighth leading cause of death in the world, and nearly one-fifth of the traffic accidents were cause by driver distractions. Inspired by the famous two-stream convolutional neural network (CNN) model, we propose a driver behavior analysis system using one spatial stream ConvNet to extract the spatial features and one temporal stream ConvNet to capture the driver’s motion information. Instead of using three-dimensional (3D) ConvNet, which would suffer from large parameters and the lack of a pre-trained model, two-dimensional (2D) ConvNet is used to construct the spatial and temporal ConvNet streams, and they were pre-trained by the large-scale ImageNet. In addition, in order to integrate different modalities, the feature-level fusion methodology was applied, and a fusion network was designed to integrate the spatial and temporal features for further classification. Moreover, a self-compiled dataset of 10 actions in the vehicle was established. According to the experimental results, the proposed system can increase the accuracy rate by nearly 30% compared to the two-stream CNN model with a score-level fusion.

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