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- 06 SepInterview Chris Kievid & Jelle Feringa in B-Nieuws #1 on Hyperbody's recent focus on Robotic Fabrication
- 16 AugHyperbody PhD candidate Alireza Hakak won the first prize in an open design competition
- 03 AugHenriette Bier and Christian Friedrich members of the reviewing committee for: Rethinking the Human in Technology-Driven Architecture
- 30 JulPublication "Architecture as a Multi-Agent System" by Tomasz Jaskiewicz in Volume #28: Internet of Things
- 28 JulInterview Kas Oosterhuis on Process, Timelessness and RealTime in Architecture
- 19 JulPaper presentation Xin Xia at the ENHSA/EAAE Conference - Rethinking the Human in Technology-Driven Architecture
- 12 JulTEDxDelft will feature Kas Oosterhuis as speaker — Ideas spreading everywhere
- 01 JulURBAN FLUX workshop @ Harbin Institute of Technology : 25th June - 9th July 2011
- 29 JunDr. Henriette Bier will be presenting her paper "Robotic Environments" at ISARC 2011
- 27 JunLecture and paper by Alireza Mahdizadeh Hakak and Nimish Biloria @ iVERG Conference
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A.Liu Cheng, H. Bier, G. Latorre, B. Kemper and D. Fischer publish a paper on A High-Resolution Intelligence Implementation based on Design-to-Robotic-Production and -Operation strategies in the 34th International Symposium on Automation and Robotics in Construction (ISARC 2017) (June 28 - July 1, 2017).
ABSTRACT: This paper presents an initial proof-of-concept implementation of a comprehensively intelligent built-environment based on mutually informing Design-to-Robotic-Production and -Operation (D2RP&O) strategies and methods developed at Delft University of Technology (TUD). In this implementation, D2RP is expressed via deliberately differentiated and function-specialized components, while D2RO expressions subsume an extended Ambient Intelligence (AmI) enabled by a Cyber-Physical System (CPS). This CPS, in turn, is built on a heterogeneous, scalable, self-healing, and partially meshed Wireless Sensor and Actuator Network (WSAN) whose nodes may be clustered dynamically ad hoc to respond to varying computational needs. Two principal and innovative functionalities are demonstrated in this implementation: (1) cost-effective yet robust Human Activity Recognition (HAR) via Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classification models, and (2) appropriate corresponding reactions that promote the occupant’s spatial experience and well-being via continuous regulation of illumination with respect to colors and intensities to correspond to engaged activities. The present implementation attempts to provide a fundamentally different approach to intelligent built-environments, and to promote a highly sophisticated alternative to existing intelligent solutions whose disconnection between architectural considerations and computational services limits their operational scope and impact.