Metaphors are good mirrors: reflecting on change for teacher educators

In this paper we discuss the use of metaphor as an educative tool for reflection. In the instance of this paper we use metaphor to reflect on the personal images of change that were used by some women teacher educators to make sense of their professional lives and practices over the last decade. This last decade in teacher education has seen significant institutional and cultural change. The paper discusses the strengths and limitations of the use of metaphor. The different interpretations of these metaphors illustrates how these women have used metaphor for explaining facets of change in their professional lives. The challenge of professional renewal is apparent in the metaphors in the ways that complexity, change, journeys, and movement are indicated. Reflection on change in professional practice needs to be continuous. Use of metaphor in the way described in this paper encourages that ongoing process.

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.

Spherical indentation hardness of shape memory alloys

Using dimensional analysis and the finite element method, the spherical indentation hardness of shape memory alloys (SMAs) is investigated. The scaling relationship between the hardness and the mechanical properties of a SMA, such as the forward transformation stress, the maximum transformation strain magnitude, has been derived. Numerical results demonstrated that the hardness increases with the indentation depth but there is no three-fold relationship between the hardness and the forward transformation stress. Increasing the maximum transformation strain magnitude would reduce the hardness of the material. These research results enhance our understanding of the hardness from the spherical indentation of SMAs.

Fuzzy clustering of non-convex patterns using global optimization

This paper discusses various extensions of the classical within-group sum of squared errors functional, routinely used as the clustering criterion. Fuzzy c-means algorithm is extended to the case when clusters have irregular shapes, by representing the clusters with more than one prototype. The resulting minimization problem is non-convex and non-smooth. A recently developed cutting angle method of global optimization is applied to this difficult problem

Convex hulls as an hypothesis language bias

Classification learning is dominated by systems which induce large numbers of small axis-orthogonal decision surfaces which biases such systems towards particular hypothesis types. However, there is reason to believe that many domains have underlying concepts which do not involve axis orthogonal surfaces. Further, the multiplicity of small decision regions mitigates against any holistic appreciation of the theories produced by these systems, notwithstanding the fact that many of the small regions are individually comprehensible. We propose the use of less strongly biased hypothesis languages which might be expected to model' concepts using a number of structures close to the number of actual structures in the domain. An instantiation of such a language, a convex hull based classifier, CHI, has been implemented to investigate modeling concepts as a small number of large geometric structures in n-dimensional space. A comparison of the number of regions induced is made against other well-known systems on a representative selection of largely or wholly continuous valued machine learning tasks. The convex hull system is shown to produce a number of induced regions about an order of magnitude less than well-known systems and very close to the number of actual concepts. This representation, as convex hulls, allows the possibility of extraction of higher level mathematical descriptions of the induced concepts, using the techniques of computational geometry.

Spherical harmonics descriptor for 2D-image retrieval

In this paper, spherical harmonics are proposed as shape descriptors for 2D images. We introduce the concept of connectivity; 2D images are decomposed using connectivity, which is followed by 3D model construction. Spherical harmonics are obtained for 3D models and used as descriptors for the underlying 2D shapes. Difference between two images is computed as the Euclidean distance between their spherical harmonics descriptors. Experiments are performed to test the effectiveness of spherical harmonics for retrieval of 2D images. Item S8 within the MPEG-7 still images content set is used for performing experiments; this dataset consists of 3621 still images. Experimental results show that the proposed descriptors for 2D images are effective

Spherical indentation of superelastic shape memory alloys

Spherical indentation of superelastic shape memory alloys (SMAs) has been theoretically analyzed. Two characteristic points on the superelastic indentation curve have been discovered. The bifurcation force corresponding to the bifurcation point relies on the forward transformation stress and the return force corresponding to the return point relies on the reverse transformation stress.
Based on these theoretical relationships, an approach to determine the transformation stresses of superelastic SMAs has been proposed. To improve the accuracy of the measurement, a slope method to locate the two characteristic points from the slope curves is further suggested. Additionally, the spherical indentation hardness was also analyzed.

Analysis of spherical indentation of superelastic shape memory alloys

Dimensional analysis and the finite element method are applied in this paper to study spherical indentation of superelastic shape memory alloys. The scaling relationships derived from dimensional analysis bridge the indentation response and the mechanical properties of a superelastic shape memory alloy. Several key variables of a superelastic indentation curve are revealed and examined. We prove that the bifurcation force in a superelastic indentation curve only relies on the forward transformation stress and the elastic properties of the initial austenite; and the return force in a superelastic indentation curve only relies on the reverse transformation stress and the elastic properties of the initial austenite. Furthermore, the dimensionless functions to determine the bifurcation force and the return force are proved to be identical. These results not only enhance our understanding of spherical indentation of superelastic shape memory alloys, but also provide the theoretical basis for developing a practicable method to calibrate the mechanical properties of a superelastic material from the spherical indentation test.

3D haptic needle insertion simulator utilising an agent based spherical voxel model

This paper describes a technique for the real-time modeling of deformable tissue. Specifically geared towards needle insertion simulation, the low computational requirements of the model enable highly accurate haptic feedback to a user without introducing noticeable time delay or buzzing generally associated with haptic surgery simulation. Using a spherical voxel array combined with aspects of computational geometry and agent communication and interaction principals, the model is capable of providing haptic update rates of over 1000Hz with real-time visual feedback. Iterating through over 1000 voxels per millisecond to determine collision and haptic response while making use of Vieta’s Theorem for extraneous force culling.

Determination of plastic yield stress from spherical indentation slope curve

The indentation slope curve from a spherical indentation on elastic-plastic materials is examined. By comparing it with that of an linear elastic material of the same elastic properties, we found that the start point of plastic yielding for an elastic-plastic material can be easily located from the indentation slope curve. Based on this analysis, a simple but effective method is proposed to measure the plastic yield stress of very small samples from a spherical nano-indentation slope curve.

Convex hulls in concept induction

Classification learning is dominated by systems which induce large numbers of small axis-orthogonal decision surfaces. This strongly biases such systems towards particular hypothesis types but there is reason believe that many domains have underlying concepts which do not involve axis orthogonal surfaces. Further, the multiplicity of small decision regions mitigates against any holistic appreciation of the theories produced by these systems, notwithstanding the fact that many of the small regions are individually comprehensible. This thesis investigates modeling concepts as large geometric structures in n-dimensional space. Convex hulls are a superset of the set of axis orthogonal hyperrectangles into which axis orthogonal systems partition the instance space. In consequence, there is reason to believe that convex hulls might provide a more flexible and general learning bias than axis orthogonal regions. The formation of convex hulls around a group of points of the same class is shown to be a usable generalisation and is more general than generalisations produced by axis-orthogonal based classifiers, without constructive induction, like decision trees, decision lists and rules. The use of a small number of large hulls as a concept representation is shown to provide classification performance which can be better than that of classifiers which use a large number of small fragmentary regions for each concept. A convex hull based classifier, CH1, has been implemented and tested. CH1 can handle categorical and continuous data. Algorithms for two basic generalisation operations on hulls, inflation and facet deletion, are presented. The two operations are shown to improve the accuracy of the classifier and provide moderate classification accuracy over a representative selection of typical, largely or wholly continuous valued machine learning tasks. The classifier exhibits superior performance to well-known axis-orthogonal-based classifiers when presented with domains where the underlying decision surfaces are not axis parallel. The strengths and weaknesses of the system are identified. One particular advantage is the ability of the system to model domains with approximately the same number of structures as there are underlying concepts. This leads to the possibility of extraction of higher level mathematical descriptions of the induced concepts, using the techniques of computational geometry, which is not possible from a multiplicity of small regions.

Haptically simulating soft tissue using spherical voxel and reactive agents

This thesis describes technology developed by the author enabling trainee surgeons to perform needle insertion procedures with force feedback (haptics) on a virtual patient. Addition of the sense of touch to medical simulation is arguably the most important step forward in the evolution of haptic technology to this day.

Fracture analysis of spherical shells : application to cracking of macadamia nuts

The main consideration in recovering the macadamia kernal is to crack the spherical nutshell without damaging the kernal. Five mechanical cracking tools were tested, and the fracture mechanisms of nutshells, under various cracking loads, were studied. A classical theoretical approach and a numerical method were both used to investigate the influence of crack face closure on the stress intensity factor for a cracked spherical shell subjected to membrane forces and bending moments.