In digital design practice, the connection and feedback between physical and digital modelling is receiving increasing attention and is seen as a source of creativity and design innovation. The authors present a workflow that supports real-time design collaboration between human and machine intelligence through physical model building. The proposed framework is investigated through a case study, where we test the direct connectivity of physical and digital modelling environments with the integration of artificial neural networks. By combining 3D capturing tools and machine learning algorithms, the research creates an instant feedback loop between human and machine, introducing a hybrid immediacy that puts physical model building back at the centre of the digitally focused design process. By fusing physical models and digital workflows, the research aims to create interactivity between data, material and designer already at the early stage of the design.
Millions of people are using the World Wide Web and are publishing content online. This user generated content contains many information relevant not only to marketing but to companies in general (customer-oriented products), governments (direct democracy) and many more. Analysis on such data becomes more and more important. This paper deals with a prerequisite: we propose an algorithm to automatically detect posting structures in flat internet fora to extract user comments. The algorithm is able to handle a wide range of different fora systems -even nested structures. The approach first detects the main content section by applying a modified version of the SST algorithm and then detects the posting structure by using several posting properties found in internet fora. It creates XPath expressions for faster data extraction in further steps.
The paper details a 3D to 2D encoding method, which can store complex digital 3D models of architecture within a single image. The proposed encoding works in combination with a point cloud notation and a sequential slicing operation where each slice of points is stored as a single row of pixels in the UV space of a 1024 × 1024 image. The performance of the notation system is compared between a StyleGan2 and existing image editing methods and evaluated through the production of new 3D models of houses with material attributes. The uncovered findings maintain the relatively high level of detail stored through the encoding while allowing for innovative ways of form-finding—producing new and unseen 3d models of architectural houses.
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