Dimensionality reduction for parametric design exploration

In architectural design, parametric models often include numeric parameters that can be adjusted to explore different design options. The resulting design space can be easily displayed to the user if the number of parameters is low, for example using a simple two or three-dimensional plot. However, visualising the design space of models defined by multiple parameters is not straightforward. In this paper it is shown how dimensionality reduction can assist in this task whilst retaining associativity between input designs in a high-dimensional parameter space. A form of dimensionality reduction based on neural networks, the Self-Organising Map (SOM) is used in combination with Rhino Grasshopper to demonstrate the approach and its potential benefits for human/machine design exploration

Feature regularization and learning for human activity recognition.

Doctoral Degree. University of KwaZulu-Natal, Durban.Feature extraction is an essential component in the design of human activity recognition model. However, relying on extracted features alone for learning often makes the model a suboptimal model. Therefore, this research work seeks to address such potential problem by investigating feature regularization. Feature regularization is used for encapsulating discriminative patterns that are needed for better and efficient model learning. Firstly, a within-class subspace regularization approach is proposed for eigenfeatures extraction and regularization in human activity recognition. In this ap- proach, the within-class subspace is modelled using more eigenvalues from the reliable subspace to obtain a four-parameter modelling scheme. This model enables a better and true estimation of the eigenvalues that are distorted by the small sample size effect. This regularization is done in one piece, thereby avoiding undue complexity of modelling eigenspectrum differently. The whole eigenspace is used for performance evaluation because feature extraction and dimensionality reduction are done at a later stage of the evaluation process. Results show that the proposed approach has better discriminative capacity than several other subspace approaches for human activity recognition. Secondly, with the use of likelihood prior probability, a new regularization scheme that improves the loss function of deep convolutional neural network is proposed. The results obtained from this work demonstrate that a well regularized feature yields better class discrimination in human activity recognition. The major contribution of the thesis is the development of feature extraction strategies for determining discriminative patterns needed for efficient model learning

Scalable allocation of safety integrity levels in automotive systems

The allocation of safety integrity requirements is an important problem in modern safety engineering. It is necessary to find an allocation that meets system level safety integrity targets and that is simultaneously cost-effective. As safety-critical systems grow in size and complexity, the problem becomes too difficult to be solved in the context of a manual process. Although this thesis addresses the generic problem of safety integrity requirements allocation, the automotive industry is taken as an application example.Recently, the problem has been partially addressed with the use of model-based safety analysis techniques and exact optimisation methods. However, usually, allocation cost impacts are either not directly taken into account or simple, linear cost models are considered; furthermore, given the combinatorial nature of the problem, applicability of the exact techniques to large problems is not a given. This thesis argues that it is possible to effectively and relatively efficiently solve the allocation problem using a mixture of model-based safety analysis and metaheuristic optimisation techniques. Since suitable model-based safety analysis techniques were already known at the start of this project (e.g. HiP-HOPS), the research focuses on the optimisation task.The thesis reviews the process of safety integrity requirements allocation and presents relevant related work. Then, the state-of-the-art of metaheuristic optimisation is analysed and a series of techniques, based on Genetic Algorithms, the Particle Swarm Optimiser and Tabu Search are developed. These techniques are applied to a set of problems based on complex engineering systems considering the use of different cost functions. The most promising method is selected for investigation of performance improvements and usability enhancements. Overall, the results show the feasibility of the approach and suggest good scalability whilst also pointing towards areas for improvement

Exploring expressive and functional capacities of knitted textiles exposed to wind influence

This study explores the design possibilities with knitted architectural textiles subjected to wind. The purpose is to investigate how such textiles could be applied to alter the usual static expression of exterior architectural and urban elements, such as\ua0facades\ua0and windbreaks. The design investigations were made on a manual knitting machine and on a CNC (computer numerically controlled)\ua0flat knitting machine. Four knitting techniques -\ua0tuck stitch, hanging stitches, false lace, and drop stitch - were explored based on their ability to create a three-dimensional effect on the surface level as well as on an architectural scale. Physical textile samples produced using those four techniques were subjected to controlled action of airflow. Digital experiments were also conducted, to probe the possibilities of digitally simulating textile behaviours in wind. The results indicate that especially the drop stitch technique exhibits interesting potentials. The variations in the drop stitch pattern generate both an aesthetic effect of volumetric expression of the textile architectural surface and seem beneficial in terms of wind speed reduction. Thus, these types of knitted textiles could be applied to design architecture that are efficient in terms of improving the aesthetic user experience and comfort in windy urban areas

DesignSense: A Visual Analytics Interface for Navigating Generated Design Spaces

Generative Design (GD) produces many design alternatives and promises novel and performant solutions to architectural design problems. The success of GD rests on the ability to navigate the generated alternatives in a way that is unhindered by their number and in a manner that reflects design judgment, with its quantitative and qualitative dimensions. I address this challenge by critically analyzing the literature on design space navigation (DSN) tools through a set of iteratively developed lenses. The lenses are informed by domain experts\u27 feedback and behavioural studies on design navigation under choice-overload conditions. The lessons from the analysis shaped DesignSense, which is a DSN tool that relies on visual analytics techniques for selecting, inspecting, clustering and grouping alternatives. Furthermore, I present case studies of navigating realistic GD datasets from architecture and game design. Finally, I conduct a formative focus group evaluation with design professionals that shows the tool\u27s potential and highlights future directions

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Urban identity through quantifiable spatial attributes: Coherence and dispersion of local identity through the comparative analysis of building block plans

The present analysis investigates whether and to what degree quantifiable spatial attributes, as expressed in plan representations, can capture elements related to the experience of spatial identity. Spatial identity is viewed as a constantly rearranging system of relations between discrete singularities. It is proposed that the structure of this system is perceived, inter alia, through its reflection in patterns of variable associations amongst constant spatial features. The examination of such patterns could thus reveal aspects of spatial identity in terms of degrees of differentiation and identification between discrete spatial unities. By combining different methods of shape and spatial analysis it is attempted to quantify spatial attributes, predominantly derived from plans, in order to illustrate patterns of interrelations between spaces through an objective automated process. Variability of methods aims at multileveled spatial descriptions, based on features related to scalar, geometrical and topological attributes of plans. The analysis focuses on the scale of the urban block as the basic modular unit for the formation of urban configurations and the issue of spatial identity is perceived through consistency and differentiation within and amongst urban neighbourhoods. The abstract representation of spatial units enables the investigation of the structure of relations, from which urban identity emerges, based on generic spatial attributes, detached from specific expressions of architectural style

Cyphers: On the Historiography of Digital Architecture

This dissertation reflects on the methods and concepts employed in constructing a history of digital architecture. By focusing on the methodological issues, it complements and expands the research developed for the monographic study Digital Architecture Beyond Computers (DABC) and the book chapter “Crypto Architecture”. In both pieces digital architecture is understood to cover a period of time that stretches well beyond the appearance of the modern digital computer (after World War Two). The notion of computing numbers and symbols to apprehend and intervene in our reality is in fact a much older idea than the invention of the modern digital computer. This dissertation reflects on the approach suggested by both writings by analysing the conceptual basis of computation in order to devise an appropriate historiographic approach to digital architecture. The aim of the investigation is to move beyond a technologically‐driven, utilitarian view of computation in favour of a more conceptual position that foregrounds computation’s fundamental logic and the role of the disciplines that informed and continue to inform it. This broader perspective aims at establishing a relation between the artifacts and the processes of digital architecture; that is, between what digital architecture is (which DABC explores through case studies in which computation and design affected one another), and how it is generated (the techniques and methods deployed to design architecture). This dissertation introduces a specific conceptual figure to articulate the historiography of digital architecture: the cypher. Cyphers address the fundamental challenges emerging from constructing a history of digital architecture, they organise the vast collections of case studies forming the history of digital architecture, foreground the conceptual motivations behind computation, and acknowledge the role that different disciplines (philosophy, logic, semiotic) have played in shaping what we call digital architecture

Models for learning reverberant environments

Reverberation is present in all real life enclosures. From our workplaces to our homes and even in places designed as auditoria, such as concert halls and theatres. We have learned to understand speech in the presence of reverberation and also to use it for aesthetics in music. This thesis investigates novel ways enabling machines to learn the properties of reverberant acoustic environments. Training machines to classify rooms based on the effect of reverberation requires the use of data recorded in the room. The typical data for such measurements is the Acoustic Impulse Response (AIR) between the speaker and the receiver as a Finite Impulse Response (FIR) filter. Its representation however is high-dimensional and the measurements are small in number, which limits the design and performance of deep learning algorithms. Understanding properties of the rooms relies on the analysis of reflections that compose the AIRs and the decay and absorption of the sound energy in the room. This thesis proposes novel methods for representing the early reflections, which are strong and sparse in nature and depend on the position of the source and the receiver. The resulting representation significantly reduces the coefficients needed to represent the AIR and can be combined with a stochastic model from the literature to also represent the late reflections. The use of Finite Impulse Response (FIR) for the task of classifying rooms is investigated, which provides novel results in this field. The aforementioned issues related to AIRs are highlighted through the analysis. This leads to the proposal of a data augmentation method for the training of the classifiers based on Generative Adversarial Networks (GANs), which uses existing data to create artificial AIRs, as if they were measured in real rooms. The networks learn properties of the room in the space defined by the parameters of the low-dimensional representation that is proposed in this thesis.Open Acces