The Space Re-Actor : walking a synthetic man through architectural space

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Leaf 85 blank.Includes bibliographical references (leaves 82-83).Spatial qualities in architectural design cannot be fully evaluated solely by observing geometrical constructs without reference to inhabitants placed inside. However, imagining what happens to those inhabitants and appreciating their movement is difficult even for trained architects. This thesis proposes a computational method for visualizing animated human reactions to physical conditions that are described in a synthetic architectural model. Its goal is to add a sense of place to the geometry, and augment the representation of its spatial quality for designers and audience. The proposed method introduces a walking scale figure in a geometric model. Through agent-based computation, it moves inside the model and displays various behaviors in reaction to spatial characteristics such as transparent surface, opaque surface, perforation and furniture. The figure is assigned a psychological profile with a different degree of sociability, and reacts to proximity and visibility of others in the same model. Today's advanced computational design tools can produce complex forms and sophisticated visualizations of light, materials and geometry. But they are not suitable for helping people to quickly study and understand a spatial design as it would be inhabited. The proposed method lays a foundation for developing a new kind of software that overcomes this shortcoming.by Taro Narahara.S.M

Virtual World16: virtual design collaboration for the intersection of academia and industry.

Over the past 13 years, the 'World16'-group has collaborated face-to-face on various challenges that architectural design faces within VR, architecture, urban design, and its delivery to the professional industries. The focus of the collaboration is to foster pathways of academic research and developments to industries and professions. In 2020, due to the restrictions of the pandemic, the group had to rethink and redevelop how to collaborate meaningfully and become resilient: the World16 collaborated akin to the Virtual Design Studios (VDS) of the Nineties for the first time exclusively virtually becoming the 'Virtual World16'. The paper presents the group's various projects that are transformative to the praxis in VR architecture, design and urban design, and critically reflects on the lessons learned from VDS-paradigm

Tool condition monitoring method by anomaly segmentation of time-frequency images using acoustic emission in small hole drilling

Tool wear leads to a reduction in dimensional accuracy and surface quality, as well as unexpected sudden tool failure. A broken tool can cause irreparable damage to an expensive workpiece, resulting in increased operating costs and production delays. Since the mechanical strength of small-diameter drills is inadequate for the load and prone to breakage, tool condition monitoring and diagnosis is important to prevent sudden tool breakage, increase productivity, and promote automation in machining process. The present work is aimed to investigate a tool condition monitoring method based on the analysis of acoustic emission (AE) signals emitted during small-hole drilling. We propose DDM (Deep feature Distribution Modeling), a method for image-level anomaly detection and anomaly segmentation in time-series signal analysis. The peck drilling experiments on SKD61 steels were performed with high-speed steel (HSS) drills. The continuous wavelet transform (CWT) was applied to generate time-frequency (TF) image of the AE signals during the drilling process. The TF images were quantified as anomaly scores using the DDM, which establishes normality by fitting a multivariate Gaussian (MVG) to pre-trained deep features. The anomaly detection capability of the DDM and the convolutional autoencoder (CAE) was compared using dummy data for validation. The digital microscope was employed to measure tool wear. Chip morphology was also observed by the laser microscopy. As the tool wear progressed, the anomaly score increased or decreased, with several sharp increases observed between holes 3805 and 3869 just prior to tool failure. An increase in the width of the shear layer spacing of the chips was also observed just prior to failure. Changes in the anomaly score associated with tool wear were more clearly identified by creating anomaly maps. The present investigation shows that waveform processing of AE signals using the CWT and anomaly detection based on the DDM are efficient methods for tool condition monitoring. Our proposed approach makes it possible to visualize the differences in anomaly states using a more subdivided layer context by generating multiple anomaly maps with deep feature vectors obtained from multiple layers