Chris P. Bowers

Classification of Energy Consumption in Buildings With Outlier Detection

In this paper, we propose an intelligent data-analysis method for modeling and prediction of daily electricity consumption in buildings. The objective is to enable a building-management system to be used for forecasting and detection of abnormal energy use. First, an outlier-detection method is proposed to identify abnormally high or low energy use in a building. Then a canonical variate analysis is employed to describe latent variables of daily electricity-consumption profiles, which can be used to group the data sets into different clusters. Finally, a simple classifier is used to predict the daily electricity-consumption profiles. A case study, based on a mixed-use environment, was studied. The results demonstrate that the method proposed in this paper can be used in conjunction with a building-management system to identify abnormal utility consumption and notify building operators in real time.

Simulating evolution with a computational model of embryogeny: obtaining robustness from evolved individuals

An evolutionary system is presented which employs an embryogeny model to evolve phenotypes in the form of layout of cells in specific patterns and shapes. It is shown that evolved phenotypes exhibit robustness to damage. How and why these traits appear is discussed and it is conjectured that it is the result of the effects of a complex mapping upon simulated evolution.

Formation of modules in a computational model of embryogeny

An investigation is conducted into the effects of a complex mapping between genotype and phenotype upon a simulated evolutionary process. A model of embryogeny is utilised to grow simple French flag like patterns. The system is shown to display a phenotypic robustness to damage and it is argued that this is a result of a modularity forming within the mapping process which causes a functional grouping of sections of the genotype

An Efficient Evolutionary Approach to Parameter Identification in a Building Thermal Model

Thermal models of buildings are often used to identify energy savings within a building. Given that a significant proportion of that energy is typically used to maintain building temperature, establishing the optimal control of the buildings thermal system is important. This requires an understanding of the thermal dynamics of the building, which is often obtained from physical thermal models. However, these models require detailed building parameters to be specified and these can often be difficult to determine. In this paper, we propose an evolutionary approach to parameter identification for thermal models that are formulated as an optimization task. A state-of-the-art evolutionary algorithm, i.e., SaNSDE+, has been developed. A fitness function is defined, which quantifies the difference between the energy-consumption time-series data that are derived from the identified parameters and that given by simulation with a set of predetermined target model parameters. In comparison with a conventional genetic algorithm, fast evolutionary programming, and two state-of-the-art evolutionary algorithms, our experimental results show that the proposed SaNSDE+ has significantly improved both the solution quality and the convergence speed, suggesting this is an effective tool for parameter identification for simulated building thermal models.

EMBRYOLOGICAL MODELLING OF THE EVOLUTION OF NEURAL ARCHITECTURE

Attempts in the past to use evolutionary simulations to model the emergence of modular neural architectures has led to conflicting results. Here, we present some preliminary work on the use of computational embryogeny to model the evolution of neural architecture at a less coarse level of description. We believe that such an approach will lead to much more reliable and biologically realistic simulations of brain evolution.

Touching annotations: A visual metaphor for navigation of annotation in digital documents

Direct touch manipulation interactions with technology are now commonplace and significant interest is building around their use in the culture and heritage domain. Such interactions can give people the opportunity to explore materials and artefacts in ways that would otherwise be unavailable. These are often heavily annotated and can be linked to a large array of related digital content, thus enriching the experience for the user. Research has addressed issues of how to present digital documents and their related annotations but at present it is unclear what the optimal interaction approach to navigating these annotations in a touch display context might be. In this paper we investigate the role of two alternative approaches to support the navigation of annotations in digitised documents in the context of a touch interface. Through a control study we demonstrate that, whilst the navigation paradigm displays a significant interaction with the type of annotations task performed, there is no discernible advantage of using a natural visual metaphor for annotation in this context. This suggests that design of digital document annotation navigation tools should account for the context and navigation tasks being considered.

Choosing your moment: interruptions in multimedia annotation

In a cooperative mixed-initiative system, timely and effective dialogue between the system and user is important to ensure that both sides work towards producing the most effective results, and this is affected by how disruptive any interruptions are as the user completes their primary task. A disruptive interaction means the user may become irritated with the system, or might take longer to deal with the interruption and provide information that the system needs to continue. Disruption is influenced both by the nature of the interaction and when it takes place in the context of the users progress through their main task. We describe an experiment based on a prototype cooperative video annotation system designed to explore the impact of interruptions, in the form of questions posed by the system that the user must address. Our findings demonstrate a preference towards questions presented in context with the content of the video, rather than at the natural opportunities presented by transitions in the video. This differs from previous research which concentrates on interruptions in the form of notifications.

User perception of interruptions in multimedia annotation tasks

For mixed-initiative multimedia annotation systems an effective dialogue between the system and user is critical. In order to inform the development of such dialogue a clear insight into the impact of interruptions upon the perceptions of the user is required. We present preliminary results of an investigation into interruptions in the form of queries to the user. We show that a user can perceive differences between trivial and important queries. Whether a query is shown in or out of context, or at some opportune time, is also shown to have an impact on user perception of the system.

Modularity in a Computational Model of Embryogeny

Natural evolution is about searching for adaptations in order to survive in a constantly changing environment. Of course, in nature these adaptations can occur at many levels from the single gene to entire populations of individuals. Adaptation can consist of rapid successful genetic changes, common in many forms of bacteria, to changes in phenotypic behaviour, such as the ability of humans to develop new skills and technologies.

CASAM: collaborative human-machine annotation of multimedia

The CASAM multimedia annotation system implements a model of cooperative annotation between a human annotator and automated components. The aim is that they work asynchronously but together. The system focuses upon the areas where automated recognition and reasoning are most effective and the user is able to work in the areas where their unique skills are required. The system’s reasoning is influenced by the annotations provided by the user and, similarly, the user can see the system’s work and modify and, implicitly, direct it. The CASAM system interacts with the user by providing a window onto the current state of annotation, and by generating requests for information which are important for the final annotation or to constrain its reasoning. The user can modify the annotation, respond to requests and also add their own annotations. The objective is that the human annotator’s time is used more effectively and that the result is an annotation that is both of higher quality and produced more quickly. This can be especially important in circumstances where the annotator has a very restricted amount of time in which to annotate the document. In this paper we describe our prototype system. We expand upon the techniques used for automatically analysing the multimedia document, for reasoning over the annotations generated and for the generation of an effective interaction with the end-user. We also present the results of evaluations undertaken with media professionals in order to validate the approach and gain feedback to drive further research.