Density based fuzzy c-means clustering of non-convex patterns

We propose a new technique to perform unsupervised data classification (clustering) based on density induced metric and non-smooth optimization. Our goal is to automatically recognize multidimensional clusters of non-convex shape. We present a modification of the fuzzy c-means algorithm, which uses the data induced metric, defined with the help of Delaunay triangulation. We detail computation of the distances in such a metric using graph algorithms. To find optimal positions of cluster prototypes we employ the discrete gradient method of non-smooth optimization. The new clustering method is capable to identify non-convex overlapped d-dimensional clusters.

Robust ODF smoothing for accurate estimation of fiber orientation

Q-ball imaging was presented as a model free, linear and multimodal diffusion sensitive approach to reconstruct diffusion orientation distribution function (ODF) using diffusion weighted MRI data. The ODFs are widely used to estimate the fiber orientations. However, the smoothness constraint was proposed to achieve a balance between the angular resolution and noise stability for ODF constructs. Different regularization methods were proposed for this purpose. However, these methods are not robust and quite sensitive to the global regularization parameter. Although, numerical methods such as L-curve test are used to define a globally appropriate regularization parameter, it cannot serve as a universal value suitable for all regions of interest. This may result in over smoothing and potentially end up in neglecting an existing fiber population. In this paper, we propose to include an interpolation step prior to the spherical harmonic decomposition. This interpolation based approach is based on Delaunay triangulation provides a reliable, robust and accurate smoothing approach. This method is easy to implement and does not require other numerical methods to define the required parameters. Also, the fiber orientations estimated using this approach are more accurate compared to other common approaches.

Prediction of pedestrians routes within a built environment in normal conditions

Modelling and prediction of pedestrian routing behaviours within known built environments has recently attracted the attention of researchers across multiple disciplines, owing to the growing demand on urban resources and requirements for efficient use of public facilities. This study presents an investigation into pedestrians' routing behaviours within an indoor environment under normal, non-panic situations. A network-based method using constrained Delaunay triangulation is adopted, and a utility-based model employing dynamic programming is developed. The main contribution of this study is the formulation of an appropriate utility function that allows an effective application of dynamic programming to predict a series of consecutive waypoints within a built environment. The aim is to generate accurate sequence waypoints for the pedestrian walking path using only structural definitions of the environment as defined in a standard CAD format. The simulation results are benchmarked against those from the A* algorithm, and the outcome positively indicates the usefulness of the proposed method in predicting pedestrians' route selection activities. © 2014 Elsevier Ltd. All rights reserved.

De Chirico and Walker in light of Nietzsche and Guattari: the enigma of partial bodies in illogical spaces

If one concedes that the Freudian unconscious is inseparable from a society attached to its past, for example, its phallocentric traditions, Guattari’s alternative model dealing with "the production of subjectivity" offers a new perspective (1995: 11). From this vantage point, it is possible to map the way "every individual and social group" models the creation of subjectivity, a subjectivity "composed of cognitive references as well as mythical, ritual and symptomatological references" (1995: 11).

Partial-state observers for nonlinear systems

This note deals with the design of reduced-order observers for a class of nonlinear systems. The order reduction of the observer is achieved by only estimating a required partial set of the state vector. Necessary and sufficient conditions are derived for the existence of reduced-order observers. An observer design procedure based on linear matrix inequalities is given. A numerical example is given to illustrate the design method.

Triangulation in organizational research : a re-presentation

This paper extends the discussion of postmodern thinking in organizational theory through a re-presentation of the concept of triangulation in organizational research. Initially triangulation is defined through the contrasting lenses of positivism and post-positivism/postmodernism and analysed as a metaphor for fixing and capturing the research subject. Subsequently triangulation is ‘re-presented’ as ‘metaphorization’—in terms of process and movement between researcher-subject positions. Rethinking the lines and angles of enquiry in triangulation, the paper suggests a shift from the ‘triangulation of distance’ tradition to a more reflexive consideration of ‘researcher stance’. This movement is represented across three perspectives: the researcher as a follower of nomothetic lines; the researcher as the taker of an ideographic overview; and the researcher as the finder of a particular angle. The implications of this re-presentation are then discussed in terms of perspective, data capture, reflexivity and metatriangulation.

Crossing the border : comparing partial and fully online learning

In many countries across the world, online learning is playing an ever-increasing role in higher education. However, there seem to be starkly contrasting analyses of the educational value of online learning. In this paper, I reflect on my own online learning experiences in the UK and Australia and conclude that there are significant differences between partial and fully online course units. I also develop general criticisms of online learning system design and suggest a number of fundamental design and performance objectives for the design of online learning systems.

Similarity solutions of partial differential equations using DESOLV

We present and describe new reduction routines included in DESOLV which, in many cases, may allow the complete automation of the determination of similarity solutions of partial differential equations.

Feasibility study of partial recrystallisation in multi-pass hot deformation process and application to calculation of mean flow stress

A feasibility study for handling the partial recrystallisation in multi-pass hot deformation where the heterogeneity of microstructure of deformed austenite is inherently accompanied is presented. The proposed model is based on modification of the conventional model in which the microstructure of deformed austenite at each pass is simply taken as being homogeneous during the multi-pass deformation. The usefulness of the modified model has been demonstrated by applying it to a four-pass oval–round (or round–oval) rod rolling sequence. The recrystallised fraction, austenite grain size (AGS) and mean flow stress at each pass computed from the modified model has been compared with those from the conventional model. The result showed that the recrystallisation behaviour and evolution of AGS at a given pass were dependent on the modelling method of the partial recrystallisation in the multi-pass rolling for the case studied. As the rolling speed increased, the difference between the mean flow stresses calculated by the conventional model and the proposed model was gradually larger in accordance with the contribution of partial recrystallisation.

Two stage partial classification for inconsistent and imbalanced classes

When deriving classification rules for a non-symmetric database with a binary target class, it is common practice to generate rules for the majority class, then any object which is not covered by a rule of suitable accuracy is by default given the minority class prediction. However, in the case where misclassification costs for the minority class significantly outweigh those of the majority class, this may mean that there are still costly incorrect predictions. We examine the capability of an evolutionary algorithm to detect these potentially costly misclassifications.

Socially optimal criminal court waiting times : a partial investigation

Criminal courts provide a forum for conducting prosecutions with a guilty plea or a trial. Since queues are used as the basis for rationing scarce court facilities delays are inevitable, however courts are invariably criticised as being inefficient as a consequence. This focus on court delay defined as the time elapsing between the listing of the case in the court list and its final disposition is misleading. Rather, attention should be drawn to the considerably longer period between the initiation of proceedings and the conclusion of the case. In the case of defendants not granted bail, this pre-trial delay confers both costs and benefits on society and this observation can be used to ascertain socially optimal pre-trial waits.