A High-Resolution Intelligence Implementation based on Design-to-Robotic-Production and -Operation Strategies

Cheng, Alexander Liu; Bier, Henriette; Latorre, Galoget; Kemper, Benjamin N.; Fischer, Daniel

doi:10.22260/isarc2017/0014

Cited by 10 publications

(12 citation statements)

References 11 publications

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“…This implementation of D2RP&O builds up on knowledge with respect to user needs [3] and wireless sensor-actuator networks for distributed climate control [4]. Such climate control functions through the use of mechanical devices that are operated by computational nodes establishing together a cyber-physical system.…”

Section: Local Comfort and Distributed Climate Controlmentioning

confidence: 99%

Developing Responsive Environments based on Design-to-Robotic-Production and -Operation Principles

Bier¹,

Nacafi²,

Zanetti³

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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The development of physical and computational mechanisms aimed at augmenting architectural environments has been one of the foci of research implemented at the Faculty of Architecture and the Built Environment, Delft University of Technology (TUD) for more than a decade. This paper presents the integration of distributed responsive climate control into the built environment based on Designto-Robotic-Production and-Operation (D2RP&O) principles. These connect computational design with robotic production and operation of buildings. In the presented case study structural elements meet loadbearing as well as functional requirements. Their spatial arrangement creates variable densities for accommodating sensor-actuators that are operating heating and cooling. This mechatronic operation relies on activity recognition for achieving responsive climate control in the built-environment.

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Section: Local Comfort and Distributed Climate Controlmentioning

confidence: 99%

Developing Responsive Environments based on Design-to-Robotic-Production and -Operation Principles

Bier¹,

Nacafi²,

Zanetti³

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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“…For example, all of the presently mentioned coordinating and router nodes may be dynamically clustered together ad hoc for Machine Learning activities as described and implemented by the authors elsewhere [12].…”

Section: Design-to-robotic-operation: Bluetooth Low Energy Wearable Nodementioning

confidence: 99%

“…The present implementation builds on a D2RP&Oenabled system architecture previously developed by the authors [12]. The Internet of Things (IoT) wearable device (wearable, henceforth) described develops the Wearables subsystem of said system architecture in order to render the user a de facto context-aware node in its Wireless Sensor and Actuator Network (WSAN), which underlies the Cyber-Physical System (CPS) that is the built-environment with its physical / computational mechanisms and services.…”

Section: Background and Introductionmentioning

confidence: 99%

Integration of a Wearable Interface in a Design-to-Robotic-Production and -Operation Development

Cheng¹,

Bier²,

Mostafavi³

2018

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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This paper presents the integration of an Internet of Things wearable device as a personal interfacing node in an intelligent built-environment framework, which is informed by Design-to-Robotic-Production and-Operation principles developed at Delft University of Technology. The device enables the user to act as an active node in the built-environment's underlying Wireless Sensor and Actuator Network, thereby permitting a more immediate and intuitive relationship between the user and his/her environment, where this latter is integrated with physical / computational adaptive systems and services. Two main resulting advantages are identified and illustrated. On the one hand, the device's sensors provide personal (i.e., body temperature / humidity, physical activity) as well as immediate environmental (i.e., personal-space airquality) data to the built-environment's embedded / ambulant systems. Moreover, rotaries on the device enable the user to override automatically established illumination and ventilation settings in order to accommodate user-preferences. On the other hand, the built-environment's systems provide notifications and feedback with respect to their status to the device, thereby raising user-awareness of the state of his/her surroundings and corresponding interior environmental conditions. In this manner, the user becomes a context-aware node in a Cyber-Physical System. The present work promotes a considered relationship between the architecture of the builtenvironment and the Information and Communication Technologies embedded and/or deployed therein in order to develop highly effective alternatives to existing Ambient Intelligence solutions.

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“…The present implementation inherits a previously developed WSAN, whose system architecture consists of four subsystems briefly summarized as follows (for more technical descriptions, see [11]):…”

Section: A Overview Of Inherited Heterogeneous Scalable Selfhealinmentioning

confidence: 99%

“…Its overall form, distribution of cavities, and densities of porosities are determined by structural optimization, Interior Environmental Quality (IEQ) [10] considerations, and the integration of anticipated ICTs. With respect to D2RO (Sections II.A, II.C, III.B), this implementation expands on the System Architecture of a previously developed prototype [11] to include objectrecognition via Deep Learning (DL) / Convolutional Neural Networks (CNNs). This vision mechanism is integrated in an inherited Fall-Detection and -Intervention System (FADIS) [12] in order to identify three human-centered as well as object-centered events and to instantiate automated interventions accordingly for the promotion of well-being.…”

Section: Introductionmentioning

confidence: 99%

Deep learning object-recognition in a design-to-robotic-production and -operation implementation

Cheng

Bier

Mostafavi

2017

2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM)

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Abstract-This paper presents a new instance in a series of discrete proof-of-concept implementations of comprehensively intelligent built-environments based on Design-to-RoboticProduction and -Operation (D2RP&O) principles developed at Delft University of Technology (TUD). With respect to D2RP, the featured implementation presents a customized design-toproduction framework informed by optimization strategies based on point clouds. With respect to D2RO, said implementation builds on a previously developed highly heterogeneous, partially meshed, self-healing, and Machine Learning (ML) enabled Wireless Sensor and Actuator Network (WSAN). In this instance, a computer vision mechanism based on open-source Deep Learning (DL) / Convolutional Neural Networks (CNNs) for object-recognition is added to the inherited ecosystem. This mechanism is integrated into the system's Fall-Detection and -Intervention System in order to enable decentralized detection of three types of events and to instantiate corresponding interventions. The first type pertains to human-centered activities / accidents, where cellular-and internet-based intervention notifications are generated in response. The second pertains to object-centered events that require the physical intervention of an automated robotic agent. Finally, the third pertains to object-centered events that elicit visual / aural notification cues for human feedback. These features, in conjunction with their enabling architectures, are intended as essential components in the on-going development of highly sophisticated alternatives to existing Ambient Intelligence (AmI) solutions.

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A High-Resolution Intelligence Implementation based on Design-to-Robotic-Production and -Operation Strategies

Cited by 10 publications

References 11 publications

Developing Responsive Environments based on Design-to-Robotic-Production and -Operation Principles

Developing Responsive Environments based on Design-to-Robotic-Production and -Operation Principles

Integration of a Wearable Interface in a Design-to-Robotic-Production and -Operation Development

Deep learning object-recognition in a design-to-robotic-production and -operation implementation

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