The Transparency of Big Data, Data Harvesting and Digital Twins

2019 ◽  
pp. 139-148 ◽  
Author(s):  
Stefan Kendzierskyj ◽  
Hamid Jahankhani ◽  
Arshad Jamal ◽  
Jaime Ibarra Jimenez
Keyword(s):  
Big Data ◽  
Digital Twins

Big Data Signal Processing and Digital Twins to Speed up Prototyping

2019 ◽  
Vol 14 (6) ◽  
pp. 38-43
Author(s):  
Christoph G. Jung ◽  
Bert Klöppel ◽  
Sascha Leidig ◽  
Wolfgang Holz
Keyword(s):  
Signal Processing ◽  
Big Data ◽  
Digital Twins ◽  
Speed Up ◽  
Data Signal

Big Data Provision for Digital Twins in Industry 4.0 Logistics Processes

2021 ◽  
Author(s):  
Paulo Figueiras ◽  
Luis Lourenco ◽  
Ruben Costa ◽  
Diogo Graca ◽  
Gisela Garcia ◽  
...  
Keyword(s):  
Big Data ◽  
Industry 4.0 ◽  
Digital Twins ◽  
Data Provision

scholarly journals Towards Digitalization in Bio-Manufacturing Operations: A Survey on Application of Big Data and Digital Twin Concepts in Denmark

2021 ◽  
Vol 3 ◽  
Author(s):  
Isuru A. Udugama ◽  
Merve Öner ◽  
Pau C. Lopez ◽  
Christan Beenfeldt ◽  
Christoph Bayer ◽  
...  
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Business Case ◽  
Digital Twin ◽  
Manufacturing Operations ◽  
Current State ◽  
Production Processes ◽  
Big Data Applications ◽  
Digital Twins ◽  
Biotech Industry

Digitalization in the form of Big Data and Digital Twin inspired applications are hot topics in today's bio-manufacturing organizations. As a result, many organizations are diverting resources (personnel and equipment) to these applications. In this manuscript, a targeted survey was conducted amongst individuals from the Danish biotech industry to understand the current state and perceived future obstacles in implementing digitalization concepts in biotech production processes. The survey consisted of 13 questions related to the current level of application of 1) Big Data analytics and 2) Digital Twins, as well as obstacles to expanding these applications. Overall, 33 individuals responded to the survey, a group spanning from bio-chemical to biopharmaceutical production. Over 73% of the respondents indicated that their organization has an enterprise-wide level plan for digitalization, it can be concluded that the digitalization drive in the Danish biotech industry is well underway. However, only 30% of the respondents reported a well-established business case for the digitalization applications in their organization. This is a strong indication that the value proposition for digitalization applications is somewhat ambiguous. Further, it was reported that digital twin applications (58%) were more widely used than Big Data analytic tools (37%). On top of the lack of a business case, organizational readiness was identified as a critical hurdle that needs to be overcome for both Digital Twin and Big Data applications. Infrastructure was another key hurdle for implementation, with only 6% of the respondents stating that their production processes were 100% covered by advanced process analytical technologies.

scholarly journals Greenhouse industry 4.0 – digital twin technology for commercial greenhouses

2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Daniel Anthony Howard ◽  
Zheng Ma ◽  
Christian Veje ◽  
Anders Clausen ◽  
Jesper Mazanti Aaslyng ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Industry 4.0 ◽  
Influential Factors ◽  
Sensor Data ◽  
Greenhouse Production ◽  
Production Chain ◽  
Improve Energy Efficiency ◽  
Digital Twin ◽  
Digital Twins

AbstractThe project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

scholarly journals Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

2021 ◽  
Vol 7 (1) ◽  
pp. 342
Author(s):  
Jia An ◽  
Chee Kai Chua ◽  
Vladimir Mironov
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Three Dimensional ◽  
3D Bioprinting ◽  
Efficient Utilization ◽  
Digital Twin ◽  
Human Organs ◽  
Cellular Resolution ◽  
Physical Experiments ◽  
Digital Twins

The application of machine learning (ML) in bioprinting has attracted considerable attention recently. Many have focused on the benefits and potential of ML, but a clear overview of how ML shapes the future of three-dimensional (3D) bioprinting is still lacking. Here, it is proposed that two missing links, Big Data and Digital Twin, are the key to articulate the vision of future 3D bioprinting. Creating training databases from Big Data curation and building digital twins of human organs with cellular resolution and properties are the most important and urgent challenges. With these missing links, it is envisioned that future 3D bioprinting will become more digital and in silico, and eventually strike a balance between virtual and physical experiments toward the most efficient utilization of bioprinting resources. Furthermore, the virtual component of bioprinting and biofabrication, namely, digital bioprinting, will become a new growth point for digital industry and information technology in future.

scholarly journals Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona’s Traffic with Empirical Data

2021 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Javier Argota Sánchez-Vaquerizo
Keyword(s):  
Big Data ◽  
Empirical Data ◽  
Large Scale ◽  
Smart Cities ◽  
Digital Twin ◽  
Fine Grained ◽  
Mobility Data ◽  
Digital Twins ◽  
Alternative Scenarios ◽  
Hourly Distribution

Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. Yet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. This paper, therefore, presents a new large-scale agent-based traffic microsimulation for the Barcelona urban area using SUMO to show the possibilities and challenges of building these scenarios based on novel fine-grained empirical big data. It combines novel mobility data from real cell phone records with conventional surveys to calibrate the model comparing two different dynamic assignment methods for getting an operationally realistic and efficient simulation. Including through traffic and the use of a stochastic adaptive routing approach results in a larger 24-hour model closer to reality. Based on an extensive multi-scalar evaluation including traffic counts, hourly distribution of trips, and macroscopic metrics, this model expands and outperforms previous large-scale scenarios, which provides new operational opportunities in city co-creation and policy. The novelty of this work relies on the effective modelling approach using newly available data and the realistic robust evaluation. This allows the identification of the fundamental challenges of simulation to accurately capture real-world dynamical systems and to their predictive power at a large scale, even when fed by big data, as envisioned by the digital twin concept applied to smart cities.

scholarly journals Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing

2021 ◽  
Vol 5 (4) ◽  
pp. 58
Author(s):  
Saman Fattahi ◽  
Takuya Okamoto ◽  
Sharifu Ura
Keyword(s):  
Surface Roughness ◽  
Big Data ◽  
Model Simulation ◽  
Semantic Annotation ◽  
Smart Manufacturing ◽  
Manufacturing Processes ◽  
Simulation Algorithm ◽  
Planning Systems ◽  
Digital Twins ◽  
Roughness Model

In smart manufacturing, human-cyber-physical systems host digital twins and IoT-based networks. The networks weave manufacturing enablers such as CNC machine tools, robots, CAD/CAM systems, process planning systems, enterprise resource planning systems, and human resources. The twins work as the brains of the enablers; that is, the twins supply the required knowledge and help enablers solve problems autonomously in real-time. Since surface roughness is a major concern of all manufacturing processes, twins to solve surface roughness-relevant problems are needed. The twins must machine-learn the required knowledge from the relevant datasets available in big data. Therefore, preparing surface roughness-relevant datasets to be included in the human-cyber-physical system-friendly big data is a critical issue. However, preparing such datasets is a challenge due to the lack of a steadfast procedure. This study sheds some light on this issue. A state-of-the-art method is proposed to prepare the said datasets for surface roughness, wherein each dataset consists of four segments: semantic annotation, roughness model, simulation algorithm, and simulation system. These segments provide input information for digital twins’ input, modeling, simulation, and validation modules. The semantic annotation segment boils down to a concept map. A human- and machine-readable concept map is thus developed where the information of other segments (roughness model, simulation algorithm, and simulation system) is integrated. The delay map of surface roughness profile heights plays a pivotal role in the proposed dataset preparation method. The successful preparation of datasets of surface roughness underlying milling, turning, grinding, electric discharge machining, and polishing shows the efficacy of the proposed method. The method will be extended to the manufacturing processes in the next phase of this study.

Deep learning for assessment of environmental satisfaction using BIM big data in energy efficient building digital twins

2022 ◽  
Vol 50 ◽  
pp. 101897
Author(s):  
Weixi Wang ◽  
Han Guo ◽  
Xiaoming Li ◽  
Shengjun Tang ◽  
Jizhe Xia ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Energy Efficient ◽  
Digital Twins ◽  
Energy Efficient Building
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