Digital Twin Collaborative Platforms: Applications to Humans-in-the-Loop Crafting of Urban Areas

Bononi, Luciano; Donatiello, Lorenzo; Longo, Danila; Massari, Martina; Montori, Federico; Stacchio, Lorenzo; Marfia, Gustavo

doi:10.1109/mce.2022.3214944

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…By superimposing extra information onto replicated scenarios, VR deepens environmental/operational understanding through pertinent data and graphical representations [12,13]. Moreover, VR boosts collaboration by facilitating interaction among users within a common space, enhancing collective deliberation and decision-making, and thereby fostering participant synergy [14][15][16]. Altogether, the synergy between IoT, DTs, and VR tools amplifies the impact on urban planning, governance, and sustainability [17].…”

Section: Introductionmentioning

confidence: 99%

Virtual Reality and Internet of Things Based Digital Twin for Smart City Cross-Domain Interoperability

del Campo,

Saavedra,

Piovano

et al. 2024

Applied Sciences

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The fusion of Internet of Things (IoT), Digital Twins, and Virtual Reality (VR) technologies marks a pivotal advancement in urban development, offering new services to citizens and municipalities in urban environments. This integration promises enhanced urban planning, management, and engagement by providing a comprehensive, real-time digital reflection of the city, enriched with immersive experiences and interactive capabilities. It enables smarter decision-making, efficient resource management, and personalized citizen services, transforming the urban landscape into a more sustainable, livable, and responsive environment. The research presented herein focuses on the practical implementation of a DT concept for managing cross-domain smart city services, leveraging VR technology to create a virtual replica of the urban environment and IoT devices. Imperative for cross-domain city services is interoperability, which is crucial not only for the seamless operation of these advanced tools but also for unlocking the potential of cross-service applications. Through the deployment of our model at the IoTMADLab facilities, we showcase the integration of IoT devices within varied urban infrastructures. The outcomes demonstrate the efficacy of VR interfaces in simplifying complex interactions, offering pivotal insights into device functionality, and enabling informed decision-making processes.

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Section: Introductionmentioning

confidence: 99%

Virtual Reality and Internet of Things Based Digital Twin for Smart City Cross-Domain Interoperability

del Campo,

Saavedra,

Piovano

et al. 2024

Applied Sciences

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“…It is important to emphasize that to develop a proper DT, the system must be able to "interact" with the environment. There is also the possibility of employing a human-in-the-loop approach [21,22]. Therefore, ML models are adopted by operators as a part of the control process [23].…”

Section: Introductionmentioning

confidence: 99%

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Gugliermetti,

Cumo,

Agostinelli

2024

Energies

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Machine learning (ML) algorithms are now part of everyday life, as many technological devices use these algorithms. The spectrum of uses is wide, but it is evident that ML represents a revolution that may change almost every human activity. However, as for all innovations, it comes with challenges. One of the most critical of these challenges is providing users with an understanding of how models’ output is related to input data. This is called “interpretability”, and it is focused on explaining what feature influences a model’s output. Some algorithms have a simple and easy-to-understand relationship between input and output, while other models are “black boxes” that return an output without giving the user information as to what influenced it. The lack of this knowledge creates a truthfulness issue when the output is inspected by a human, especially when the operator is not a data scientist. The Building and Construction sector is starting to face this innovation, and its scientific community is working to define best practices and models. This work is intended for developing a deep analysis to determine how interpretable ML models could be among the most promising future technologies for the energy management in built environments.

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Empowering Zero-Shot Object Detection: A Human-in-the-Loop Strategy for Unveiling Unseen Realms in Visual Data

Kerdvibulvech,

2024

Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management

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Digital Twin Collaborative Platforms: Applications to Humans-in-the-Loop Crafting of Urban Areas

Cited by 5 publications

References 30 publications

Virtual Reality and Internet of Things Based Digital Twin for Smart City Cross-Domain Interoperability

Virtual Reality and Internet of Things Based Digital Twin for Smart City Cross-Domain Interoperability

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

Empowering Zero-Shot Object Detection: A Human-in-the-Loop Strategy for Unveiling Unseen Realms in Visual Data

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