Comparing stochastic design decision belief models : pointwise versus interval probabilities.

Decision support systems can either directly support a product designer or support an agent operating within a multi-agent system (MAS). Stochastic based decision support systems require an underlying belief model that encodes domain knowledge. The underlying supporting belief model has traditionally been a probability distribution function (PDF) which uses pointwise probabilities for all possible outcomes. This can present a challenge during the knowledge elicitation process. To overcome this, it is proposed to test the performance of a credal set belief model. Credal sets (sometimes also referred to as p-boxes) use interval probabilities rather than pointwise probabilities and therefore are more easier to elicit from domain experts. The PDF and credal set belief models are compared using a design domain MAS which is able to learn, and thereby refine, the belief model based on its experience. The outcome of the experiment illustrates that there is no significant difference between the PDF based and credal set based belief models in the performance of the MAS

Travelling and standing waves in magnetoconvection

The problem of Boussinesq magnetoconvection with periodic boundary conditions is studied using standard perturbation techniques. It is fbund that either travelling waves or standing waves can be stable at the onset of oscillatory convection, depending on the parameters of the problem. When travelling waves occur, a steady shearing flow is present that is quadratic in the amplitude of the convective flow. The weakly nonlinear predictions are confirmed by comparison with numerical solutions of the full partial differential equations at Rayleigh numbers 10% above critical. Modulated waves (through which stability is transferred between travelling and standing waves) are found near the boundary between the regions in parameter space where travelling waves and standing waves are preferred

Analysis of customer profiles on an electrical distribution network.

It has become increasingly important for electrical distribution companies to understand the drivers of demand. The maximum demand at any given substation can vary materially on an annual basis which means it is difficult to create a load related investment plan that is robust and stable. Currently, forecasts are based only on historical demand with little understanding about contributions to load profiles. In particular, the unique diversity of customers on any particular substation can affect load profile shape and future forecasts. Domestic and commercial customers can have very different behaviours generally and within these groups there is room for variation due to economic conditions and building types. This paper analyses customer types associated to substations on a distribution network by way of principal component analysis and identification of substations which deviate from the national demand trend. By examining the variance spread of this deviation, data points can be labelled in the principal component space. Groups of substations can then be categorised as having typical or atypical load profiles. This will support the need for further investigation into particular customer types and highlight the key factors of customer categorisation

Machine learning stochastic design models.

Due to the fluid nature of the early stages of the design process, it is difficult to obtain deterministic product design evaluations. This is primarily due to the flexibility of the design at this stage, namely that there can be multiple interpretations of a single design concept. However, it is important for designers to understand how these design concepts are likely to fulfil the original specification, thus enabling the designer to select or bias towards solutions with favourable outcomes. One approach is to create a stochastic model of the design domain. This paper tackles the issues of using a product database to induce a Bayesian model that represents the relationships between the design parameters and characteristics. A greedy learning algorithm is presented and illustrated using a simple case study

Solar magnetoconvection

In recent years the study of how magnetic fields interact with thermal convection in the Sun has made significant advances. These are largely due to the rapidly increasing computer power and its application to more physically relevant parameters regimes and to more realistic physics and geometry in numerical models. Here we present a survey of recent results following one line of investigations and discuss and compare the results of these with observed phenomena

Challenges to Bayesian decision support using morphological matrices for design: empirical evidence

A novel Bayesian design support tool is empirically investigated for its potential to support the early design stages. The design support tool provides dynamic guidance with the use of morphological design matrices during the conceptual or preliminary design stages. This paper tests the appropriateness of adopting a stochastic approach for supporting the early design phase. The rationale for the stochastic approach is based on the uncertain nature of the design during this part of the design process. The support tool is based on Bayesian belief networks (BBNs) and uses a simple but effective information content–based metric to learn or induce the model structure. The dynamically interactive tool is assessed with two empirical trials. First, the laboratory-based trial with novice designers illustrates a novel emergent design search methodology. Second, the industrial-based trial with expert designers illustrates the hurdles that are faced when deploying a design support tool in a highly pressurised industrial environment. The conclusion from these trials is that there is a need for designers to better understand the stochastic methodology for them to both be able to interpret and trust the BBN model of the design domain. Further, there is a need for a lightweight domain-specific front end interface is needed to enable a better fit between the generic support tool and the domain-specific design process and associated tools

Development of a simple information pump.

The Information Pump (IP) is a methodology that aims to counter the problems arising from traditional subjective product data collection. The IP is a game theory based process that aims to maximise information extracted from a panel of subjects, while maintaining their interest in the process through a continuous panelist scoring method. The challenge with implementing this arises from the difficulty in executing the 'game'. In its original format, there is an assumption that the game is played with each player using their own PC to interact with the process. While this in theory allows information and scores to flow in a controlled manner between the players, it actually provides a major barrier to the wider adoption of the IP method. This barrier is two-fold: it is costly and complex, and it is not a natural manner for exchanging information. The core objective is to develop a low cost version of the IP method. This will use the game theory approach to maintain interest among participants and maximise information extraction, but remove the need for each participant to have their own PC interface to the game. This will require replacing both the inter-player communication method and the score keeping/reporting

Compressible magnetoconvection in three dimensions: pattern formation in a strongly stratified layer

The interaction between magnetic fields and convection is interesting both because of its astrophysical importance and because the nonlinear Lorentz force leads to an especially rich variety of behaviour. We present several sets of computational results for magnetoconvection in a square box, with periodic lateral boundary conditions, that show transitions from steady convection with an ordered planform through a regime with intermittent bursts to complicated spatiotemporal behaviour. The constraints imposed by the square lattice are relaxed as the aspect ratio is increased. In wide boxes we find a new regime, in which regions with strong fields are separated from regions with vigorous convection. We show also how considerations of symmetry and associated group theory can be used to explain the nature of these transitions and the sequence in which the relevant bifurcations occur

Analysis of clustering techniques on load profiles for electrical distribution

The classification of electrical load profiles has become increasingly important as a driver for distribution companies in understanding substation data. The daily load profile can often give great insight into the types of customers connected to the substation and can assist with developing a long-term forecast. The literature in this area often uses data mining and clustering techniques to determine a load diagram representative for a subset of customers or substations. The type of technique used can often lead to representative load diagrams of unique shapes with differing numbers of customers belonging to each group. This paper analyses clustering techniques on representative load diagrams for primary substations at the distribution level. In particular, this paper will analyse clustering techniques in terms of their performance and effect on load profile groupings. The results show that K-means clustering showed the best performance in generating unique, well-populated cluster groups. This gives a greater understanding of the divisions between substations which can be used for future forecasting