Simulation based knowledge elicitation:effect of visual representation and model parameters

Since much knowledge is tacit, eliciting knowledge is a common bottleneck during the development of knowledge-based systems. Visual interactive simulation (VIS) has been proposed as a means for eliciting experts’ decision-making by getting them to interact with a visual simulation of the real system in which they work. In order to explore the effectiveness and efficiency of VIS based knowledge elicitation, an experiment has been carried out with decision-makers in a Ford Motor Company engine assembly plant. The model properties under investigation were the level of visual representation (2-dimensional, 2½-dimensional and 3-dimensional) and the model parameter settings (unadjusted and adjusted to represent more uncommon and extreme situations). The conclusion from the experiment is that using a 2-dimensional representation with adjusted parameter settings provides the better simulation-based means for eliciting knowledge, at least for the case modelled.

Issues of collaboration, representation, meaning and emotions:utilising participant-led visual diaries to capture the everyday lives of people in mid to later life

In recent years there has been an increasing use of visual methods in ageing research. There are, however, limited reflections and critical explorations of the implications of using visual methods in research with people in mid to later life. This paper examines key methodological complexities when researching the daily lives of people as they grow older and the possibilities and limitations of using participant-generated visual diaries. The paper will draw on our experiences of an empirical study, which included a sample of 62 women and men aged 50 years and over with different daily routines. Participant-led photography was drawn upon as a means to create visual diaries, followed by in-depth, photo-elicitation interviews. The paper will critically reflect on the use of visual methods for researching the daily lives of people in mid to later life, as well as suggesting some wider tensions within visual methods that warrant attention. First, we explore the extent to which photography facilitates a ‘collaborative’ research process; second, complexities around capturing the ‘everydayness’ of daily routines are explored; third, the representation and presentation of ‘self’ by participants within their images and interview narratives is examined; and, finally, we highlight particular emotional considerations in visualising daily life.

Bayesian filtering over compressed appearance states

This paper presents a framework for performing real-time recursive estimation of landmarks’ visual appearance. Imaging data in its original high dimensional space is probabilistically mapped to a compressed low dimensional space through the definition of likelihood functions. The likelihoods are subsequently fused with prior information using a Bayesian update. This process produces a probabilistic estimate of the low dimensional representation of the landmark visual appearance. The overall filtering provides information complementary to the conventional position estimates which is used to enhance data association. In addition to robotics observations, the filter integrates human observations in the appearance estimates. The appearance tracks as computed by the filter allow landmark classification. The set of labels involved in the classification task is thought of as an observation space where human observations are made by selecting a label. The low dimensional appearance estimates returned by the filter allow for low cost communication in low bandwidth sensor networks. Deployment of the filter in such a network is demonstrated in an outdoor mapping application involving a human operator, a ground and an air vehicle.

Applying ISOMAP to the learning of hyperspectral image

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.

A security system based on human iris identification using wavelet transform

A security system based on the recognition of the iris of human eyes using the wavelet transform is presented. The zero-crossings of the wavelet transform are used to extract the unique features obtained from the grey-level profiles of the iris. The recognition process is performed in two stages. The first stage consists of building a one-dimensional representation of the grey-level profiles of the iris, followed by obtaining the wavelet transform zerocrossings of the resulting representation. The second stage is the matching procedure for iris recognition. The proposed approach uses only a few selected intermediate resolution levels for matching, thus making it computationally efficient as well as less sensitive to noise and quantisation errors. A normalisation process is implemented to compensate for size variations due to the possible changes in the camera-to-face distance. The technique has been tested on real images in both noise-free and noisy conditions. The technique is being investigated for real-time implementation, as a stand-alone system, for access control to high-security areas.

Tragedy and assimilation: Occupying the patterned surface

This chapter draws on biographical data about two notable pattern designers of wall surfaces in the interior. Both had personal histories of multiple careers and geographical locations and both their lives ended in mysterious circumstances. One of the pattern designers, Jim Thompson, disappeared in the Malaysian highlands in 1967 and was never found. The other, Florence Broadhurst, was brutally murdered in 1977; her case remains unsolved. This chapter theorizes that the patterned surface attracted Broadhurst and Thompson as a space to occupy and record their divergent pasts, and questions what it is to lose oneself in the surface of the interior, to find freedom (or slavery) in the abdication of control. This notion is further evidenced in creative works, including the Australian film Candy and the work by skin illustrator Emma Hack. What is it to work with the self as a two-dimensional representation in the outside world? Occupying the surface suggests a reflexive relationship with identity, that makes-over and re-shapes truths, lies and re-constructions. The chapter reminds us that the surface is never in stasis.

Simple Structural And Bonding Representations Of Fullerenes

Bonding in buckminsterfullerene, C60, can be described in terms of a unique canonical representation in which all six membered rings have a benzenoid Kekule structure while the pentagons are all made of exclusively single bonds. The corresponding valence bond structure reflects the full symmetry of the molecule and is consistent with the observed bond length variations. Computational support for the bonding description is provided using localized MO's obtained at the MNDO level. The requirement of benzenoid structures for all the hexagons can be used as a criterion of stability of fullerenes which complements the pentagon isolation rule. A convenient two-dimensional representation of the fullerene structures incorporating the above bonding description is suggested, especially for use in mechanistic discussions.

Modified Kanerva model. Results for real time word recognition

This paper describes results obtained using the modified Kanerva model to perform word recognition in continuous speech after being trained on the multi-speaker Alvey 'Hotel' speech corpus. Theoretical discoveries have recently enabled us to increase the speed of execution of part of the model by two orders of magnitude over that previously reported by Prager & Fallside. The memory required for the operation of the model has been similarly reduced. The recognition accuracy reaches 95% without syntactic constraints when tested on different data from seven trained speakers. Real time simulation of a model with 9,734 active units is now possible in both training and recognition modes using the Alvey PARSIFAL transputer array. The modified Kanerva model is a static network consisting of a fixed nonlinear mapping (location matching) followed by a single layer of conventional adaptive links. A section of preprocessed speech is transformed by the non-linear mapping to a high dimensional representation. From this intermediate representation a simple linear mapping is able to perform complex pattern discrimination to form the output, indicating the nature of the speech features present in the input window.

Turn-off failure mechanism in large area IGCTs

The destruction mechanism in large area IGCTs (Integrated Gate Commutated Thyristors) under inductive switching conditions is analyzed in detail. The three-dimensional nature of the turn-off process in a 91mm diameter wafer is simulated with a two-dimensional representation. Simulation results show that the final destruction is caused by the uneven dynamic avalanche current distribution across the wafer. © 2011 IEEE.

Shape Recipes: Scene Representations that Refer to the Image

The goal of low-level vision is to estimate an underlying scene, given an observed image. Real-world scenes (e.g., albedos or shapes) can be very complex, conventionally requiring high dimensional representations which are hard to estimate and store. We propose a low-dimensional representation, called a scene recipe, that relies on the image itself to describe the complex scene configurations. Shape recipes are an example: these are the regression coefficients that predict the bandpassed shape from bandpassed image data. We describe the benefits of this representation, and show two uses illustrating their properties: (1) we improve stereo shape estimates by learning shape recipes at low resolution and applying them at full resolution; (2) Shape recipes implicitly contain information about lighting and materials and we use them for material segmentation.

Tracking Human Body Pose on a Learned Smooth Space

Particle filtering is a popular method used in systems for tracking human body pose in video. One key difficulty in using particle filtering is caused by the curse of dimensionality: generally a very large number of particles is required to adequately approximate the underlying pose distribution in a high-dimensional state space. Although the number of degrees of freedom in the human body is quite large, in reality, the subset of allowable configurations in state space is generally restricted by human biomechanics, and the trajectories in this allowable subspace tend to be smooth. Therefore, a framework is proposed to learn a low-dimensional representation of the high-dimensional human poses state space. This mapping can be learned using a Gaussian Process Latent Variable Model (GPLVM) framework. One important advantage of the GPLVM framework is that both the mapping to, and mapping from the embedded space are smooth; this facilitates sampling in the low-dimensional space, and samples generated in the low-dimensional embedded space are easily mapped back into the original highdimensional space. Moreover, human body poses that are similar in the original space tend to be mapped close to each other in the embedded space; this property can be exploited when sampling in the embedded space. The proposed framework is tested in tracking 2D human body pose using a Scaled Prismatic Model. Experiments on real life video sequences demonstrate the strength of the approach. In comparison with the Multiple Hypothesis Tracking and the standard Condensation algorithm, the proposed algorithm is able to maintain tracking reliably throughout the long test sequences. It also handles singularity and self occlusion robustly.

General, PDB-based collective variables for protein folding

New, automated forms of data-analysis are required in order to understand the high-dimensional trajectories that are obtained from molecular dynamics simulations on proteins. Dimensionality reduction algorithms are particularly appealing in this regard as they allow one to construct unbiased, low-dimensional representations of the trajectory using only the information encoded in the trajectory. The downside of this approach is that different sets of coordinates are required for each different chemical systems under study precisely because the coordinates are constructed using information from the trajectory. In this paper we show how one can resolve this problem by using the sketch-map algorithm that we recently proposed to construct a low-dimensional representation of the structures contained in the protein data bank (PDB). We show that the resulting coordinates are as useful for analysing trajectory data as coordinates constructed using landmark configurations taken from the trajectory and that these coordinates can thus be used for understanding protein folding across a range of systems.

The Human Figure in Drawing and Sculpture

My project as a Senior Scholar has been the study of the human figure through drawing and sculpture. I have worked directly from the model in order to understand the form and structure of the human body. The result of this concentrated study has been a sharpened sense of vision and an increased confidence in the use of materials for both two and three-dimensional representation.

Design gráfico da pluma de contaminação por compostos orgânicos voláteiss utilizando software CAD na investigação de passivo ambiental em um posto revendedor de combustíveis em Natal/RN

Among the potentially polluting economic activities that compromise the quality of groundwater are the gas stations. The city of Natal has about 120 gas stations, of which only has an environmental license for operation. Discontinuities in the offices were notified by the Public Ministry of Rio Grande do Norte to carry out the environmental adaptations, among which is the investigation of environmental liabilities. The preliminary and confirmatory stages of this investigation consisted in the evaluation of soil gas surveys with two confirmatory chemical analysis of BTEX, PAH and TPH. To get a good evaluation and interpretation of results obtained in the field, it became necessary three-dimensional representation of them. We used a CAD software to graph the equipment installed in a retail service station fuel in Natal, as well as the plumes of contamination by volatile organic compounds. The tool was concluded that contamination is not located in the current system of underground storage of fuel development, but reflects the historical past in which tanks were removed not tight gasoline and diesel

Mapeamento robótico 2,5-D com representação em grade de ocupação-elevação

This work introduces a new method for environment mapping with three-dimensional information from visual information for robotic accurate navigation. Many approaches of 3D mapping using occupancy grid typically requires high computacional effort to both build and store the map. We introduce an 2.5-D occupancy-elevation grid mapping, which is a discrete mapping approach, where each cell stores the occupancy probability, the height of the terrain at current place in the environment and the variance of this height. This 2.5-dimensional representation allows that a mobile robot to know whether a place in the environment is occupied by an obstacle and the height of this obstacle, thus, it can decide if is possible to traverse the obstacle. Sensorial informations necessary to construct the map is provided by a stereo vision system, which has been modeled with a robust probabilistic approach, considering the noise present in the stereo processing. The resulting maps favors the execution of tasks like decision making in the autonomous navigation, exploration, localization and path planning. Experiments carried out with a real mobile robots demonstrates that this proposed approach yields useful maps for robot autonomous navigation