Design optimisation using convex programming: Towards waste-efficient building designs

© 2019 The Authors A non-modular building layout is amongst the leading sources of offcut waste, resulting from a substantial amount of onsite cutting and fitting of bricks, blocks, plasterboard, and tiles. The field of design for dimensional coordination is concerned with finding an optimal configuration for non-overlapping spaces in the layout to reduce materials waste. In this article, we propose a convex optimisation-based algorithm for finding alternative floor layouts to enforce the design for dimensional coordination. At the crux of the proposed algorithm lies two mathematical models. The first is the convex relaxation model that establishes the topology of spaces within the layout through relative positioning constraints. We employed acyclic graphs to generate a minimal set of relative positioning constraints to model the problem. The second model optimises the geometry of spaces based on the modular size. The algorithm exploits aspect ratio constraints to restrict the generation of alternate layouts with huge variations. The algorithm is implemented in the BIMWaste tool for automating the design exploration process. BIMWaste is capable of investigating the degree to which designers consider dimensional coordination. We tested the algorithm over 10 completed building projects to report its suitability and accuracy. The algorithm generates competitive floor layouts for the same client intent that are likely to be tidier and more modular. More importantly, those floor layouts have improved waste performance (i.e., 8.75% less waste) due to a reduced tendency for material cutting and fitting. This study, for the first time, used convex programming for the design optimisation with a focus to reduce construction waste

FROM MUSIC INFORMATION RETRIEVAL (MIR) TO INFORMATION RETRIEVAL FOR MUSIC (IRM)

This thesis reviews and discusses certain techniques from the domain of (Music) Information Retrieval, in particular some general data mining algorithms. It also describes their specific adaptations for use as building blocks in the CACE4 software application. The use of Augmented Transition Networks (ATN) from the field of (Music) Information Retrieval is, to a certain extent, adequate as long as one keeps the underlying tonal constraints and rules as a guide to understanding the structure one is looking for. However since a large proportion of algorithmic music, including music composed by the author, is atonal, tonal constraints and rules are of little use. Analysis methods from Hierarchical Clustering Techniques (HCT) such as k-means and Expectation-Maximisation (EM) facilitate other approaches and are better suited for finding (clustered) structures in large data sets. ART2 Neural Networks (Adaptive Resonance Theory) for example, can be used for analysing and categorising these data sets. Statistical tools such as histogram analysis, mean, variance as well as correlation calculations can provide information about connections between members in a data set. Altogether this provides a diverse palette of usable data analysis methods and strategies for creating algorithmic atonal music. Now acting as (software) strategy tools, their use is determined by the quality of their output within a musical context, as demonstrated when developed and programmed into the Computer Assisted Composition Environment: CACE4. Music Information Retrieval techniques are therefore inverted: their specific techniques and associated methods of Information Retrieval and general data mining are used to access the organisation and constraints of abstract (non-specific musical) data in order to use and transform it in a musical composition

Applying blended conceptual spaces to variable choice and aesthetics in data visualisation

Computational creativity is an active area of research within the artificial intelligence domain that investigates what aspects of computing can be considered as an analogue to the human creative process. Computers can be programmed to emulate the type of things that the human mind can. Artificial creativity is worthy of study for two reasons. Firstly, it can help in understanding human creativity and secondly it can help with the design of computer programs that appear to be creative. Although the implementation of creativity in computer algorithms is an active field, much of the research fails to specify which of the known theories of creativity it is aligning with. The combination of computational creativity with computer generated visualisations has the potential to produce visualisations that are context sensitive with respect to the data and could solve some of the current automation problems that computers experience. In addition theories of creativity could theoretically compute unusual data combinations, or introducing graphical elements that draw attention to the patterns in the data. More could be learned about the creativity involved as humans go about the task of generating a visualisation. The purpose of this dissertation was to develop a computer program that can automate the generation of a visualisation, for a suitably chosen visualisation type over a small domain of knowledge, using a subset of the computational creativity criteria, in order to try and explore the effects of the introduction of conceptual blending techniques. The problem is that existing computer programs that generate visualisations are lacking the creativity, intuition, background information, and visual perception that enable a human to decide what aspects of the visualisation will expose patterns that are useful to the consumer of the visualisation. The main research question that guided this dissertation was, “How can criteria derived from theories of creativity be used in the generation of visualisations?”. In order to answer this question an analysis was done to determine which creativity theories and artificial intelligence techniques could potentially be used to implement the theories in the context of those relevant to computer generated visualisations. Measurable attributes and criteria that were sufficient for an algorithm that claims to model creativity were explored. The parts of the visualisation pipeline were identified and the aspects of visualisation generation that humans are better at than computers was explored. Themes that emerged in both the computational creativity and the visualisation literature were highlighted. Finally a prototype was built that started to investigate the use of computational creativity methods in the ‘variable choice’, and ‘aesthetics’ stages of the data visualisation pipeline.School of ComputingM. Sc. (Computing

Web Design for the USDC of RI

This project consisted of the redesign of the United States District Court of Rhode Island\u27s website and the creation of the Rhode Island Probation Office\u27s website. The websites create a user-friendly experience for both the staff of the court office\u27s as well as the general public. The newly redesigned websites are easy to navigate and find all information on each website quickly and easily. The USDC of RI website was redesigned to help organize the content from their old website in a more effective manner and the Probation Office website was created from scratch to help distribute commonly used forms and information which will save time and money in the future

Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation