Investigating feasible tools for swarm pattern transformation

Abstract-The work reported in this paper is motivated by the need for developing swarm pattern transformation methodologies. Two methods, namely a macroscopic method and a mathematical method are investigated for pattern transformation. The first method is based on macroscopic parameters while the second method is based on both microscopic and macroscopic parameters. A formal definition to pattern transformation considering four special cases of transformation is presented. Simulations on a physics simulation engine are used to confirm the feasibility of the proposed transformation methods. A brief comparison between the two methods is also presented.

Modified radial basis function neural network using output transformation

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.

A method for incorporating climate variability in climate change impact assessments: sensitivity of river flows in the Eden catchment to precipitation scenarios

Interest in the impacts of climate change is ever increasing. This is particularly true of the water sector where understanding potential changes in the occurrence of both floods and droughts is important for strategic planning. Climate variability has been shown to have a significant impact on UK climate and accounting for this in future climate cahgne projections is essential to fully anticipate potential future impacts. In this paper a new resampling methodology is developed which includes the variability of both baseline and future precipitation. The resampling methodology is applied to 13 CMIP3 climate models for the 2080s, resulting in an ensemble of monthly precipitation change factors. The change factors are applied to the Eden catchment in eastern Scotland with analysis undertaken for the sensitivity of future river flows to the changes in precipitation. Climate variability is shown to influence the magnitude and direction of change of both precipitation and in turn river flow, which are not apparent without the use of the resampling methodology. The transformation of precipitation changes to river flow changes display a degree of non-linearity due to the catchment's role in buffering the response. The resampling methodology developed in this paper provides a new technique for creating climate change scenarios which incorporate the important issue of climate variability.

Technical Note: The normal quantile transformation and its application in a flood forecasting system.

The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Distribution Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as the Normal-Score Transform. In this paper some possible problems caused by small sample sizes when applying the NQT in flood forecasting systems will be discussed and a novel way to solve the problem will be outlined by combining extreme value analysis and non-parametric regression methods. The method will be illustrated by examples of hydrological stream-flow forecasts.

A nonparametric ensemble transform method for Bayesian inference

Many applications, such as intermittent data assimilation, lead to a recursive application of Bayesian inference within a Monte Carlo context. Popular data assimilation algorithms include sequential Monte Carlo methods and ensemble Kalman filters (EnKFs). These methods differ in the way Bayesian inference is implemented. Sequential Monte Carlo methods rely on importance sampling combined with a resampling step, while EnKFs utilize a linear transformation of Monte Carlo samples based on the classic Kalman filter. While EnKFs have proven to be quite robust even for small ensemble sizes, they are not consistent since their derivation relies on a linear regression ansatz. In this paper, we propose another transform method, which does not rely on any a priori assumptions on the underlying prior and posterior distributions. The new method is based on solving an optimal transportation problem for discrete random variables. © 2013, Society for Industrial and Applied Mathematics

An ASIFT-based local registration method for satellite imagery

Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensing applications in which ground control points (GCPs) are not always available, such as in selecting a landing zone on an outer space planet. In this paper, a framework for localization in image registration is developed. It strengthened the local registration accuracy from two aspects: less reprojection error and better feature point distribution. Affine scale-invariant feature transform (ASIFT) was used for acquiring feature points and correspondences on the input images. Then, a homography matrix was estimated as the transformation model by an improved random sample consensus (IM-RANSAC) algorithm. In order to identify a registration region with a better spatial distribution of feature points, the Euclidean distance between the feature points is applied (named the S criterion). Finally, the parameters of the homography matrix were optimized by the Levenberg–Marquardt (LM) algorithm with selective feature points from the chosen registration region. In the experiment section, the Chang’E-2 satellite remote sensing imagery was used for evaluating the performance of the proposed method. The experiment result demonstrates that the proposed method can automatically locate a specific region with high registration accuracy between input images by achieving lower root mean square error (RMSE) and better distribution of feature points.

A collocation method for solving singular integro-differential equations

This paper describes a collocation method for numerically solving Cauchy-type linear singular integro-differential equations. The numerical method is based on the transformation of the integro-differential equation into an integral equation, and then applying a collocation method to solve the latter. The collocation points are chosen as the Chebyshev nodes. Uniform convergence of the resulting method is then discussed. Numerical examples are presented and solved by the numerical techniques.

Dynamic adjustment of ferrite grains during dynamic strain induced transformation

The dynamic adjustment of ferrite grains formed during 'dynamic strain induced transformation (DSIT)' is an important feature of this mechanism that has not been addressed previously. A novel experimental method was applied to follow the effect of deformation at different stages on ferrite formed initially through DSIT. It is shown that while the continuous dynamic recrystallisation (CDRX) appears to be an acceptable mechanism for re-refinement of coarser grain size (i.e. dα>2dDSIT), it cannot explain the steady state grain size for finer ferrite grains (i.e. dα<2dDSIT). Other potential mechanisms involved in this phenomenon are examined.

A transformation model for heterogeneous servers

One of the characteristics of the current Web services is that many clients request the same or similar service from a group of replicated servers, e.g. music or movie downloading in peer-to-peer networks. Most of the time, servers are heterogeneous ones in terms of service rate. Much of research has been done in the homogeneous environment. However, there is has been little done on the heterogeneous scenario. It is important and urgent that we have models for heterogeneous server groups for the current Internet applications design and analysis. In this paper, we deploy an approximation method to transform heterogeneous systems into a group of homogeneous system. As a result, the previous results of homogeneous studies can be applied in heterogeneous cases. In order to test the approximation ratio of the proposed model to real applications, we conducted simulations to obtain the degree of similarity. We use two common strategies: random selection algorithm and Firs-Come-First-Serve (FCFS) algorithm to test the approximation ratio of the proposed model. The simulations indicate that the approximation model works well.

Multiphase material modelling by multiscale particle-in-cell method

A particle-based method for multiscale modeling of multiphase materials such as Dual Phase (DP) and Transformation Induced Plasticity (TRIP) steels has been developed. The multiscale Particle-In-Cell (PIC) method benefits from the many advantages of the FEM and mesh-free methods, and to bridge the micro and macro scales through homogenization. The conventional mesh-based modeling methods fail to give reasonable and accurate predictions for materials with complex microstructures. Alternatively in the multiscale PIC method, the Lagrangian particles moving in an Eulerian grid represent the material deformation at both the micro and macro scales. The uniaxial tension test of two phase and three-phase materials was simulated and compared with FE based simulations. The predictions using multiscale PIC method showed that accuracy of field variables could be improved by up to 7%. This can lead to more accurate forming and springback predictions for materials with important multiphase microstructural effects.

A method for automatically creating 3D animated scenes from annotated fiction text

This paper describes a strategy for automatically converting fiction text into 3D animations. It assumes the existence of fiction text annotated with avatar, object, setting, transition and relation annotations, and presents a transformation process that converts annotated text into quantified constraint systems, the solutions to which are used in the population of 3D environments. Constraint solutions are valid over temporal intervals, ensuring that consistent dynamic behaviour is produced. A substantial level of automation is achieved, while providing opportunities for creative manual intervention in animation process. The process is demonstrated using annotated examples drawn from popular fiction text that are converted into animation sequences, confirming that the desired results can be achieved with only highlevel human direction.

We close towns for a living : spatial transformation and the Tour de France

This paper explores the ways in which the passage of the Tour de France bicycle race through France produces a distinctive cultural cartography or social map of France. Drawing on Lefebvre's (1991) conceptual triad of spatial practice, representations of space and representational spaces, the paper argues that the Tour de France both represents and is a space that is annually reordered and structured by very particular cultural practices. Through an analysis of the process (and politics) of route selection, the incorporation of iconic landscape and the transformation of civic space as the race moves across the country, the paper foregrounds the socially constructed nature of map making and the role of human intervention in producing and reproducing key cultural cartographies of France through the Tour de France.

Exploring first year teachers’ identity transformation through theatre-based research

Why are beginning teachers leaving the profession in large numbers? Are they leaving because of their dissatisfaction with teaching? Are they leaving because of the conditions of their work that shape their identity? Teacher identity work emphasises it is important beginning teachers understand their professional identity as something shifting, fluid and emerging – not fixed. These and other water metaphors – such as ‘washout’, ‘sink or swim’, and ‘thrown in the deep end’ – are often used to describe beginning teachers’ experiences. Such words and metaphors assist to portray the fluid and unpredictable nature of identity transformation. However, these survival terms also influence beginning teachers to believe that their transition to teaching will be difficult. Recently there has been an increased concern over beginning teacher attrition linked to the difficulties they encounter in their early years of teaching. Yet the conditions of beginning teachers’ work in Victorian schools in Australia – including the contractual nature of employment of first year (1yr) teachers – encourage these 1yr practitioners to view their work as semi-permanent. As a result these 1yr teachers do not see themselves as teaching for extended periods of time, as was once the case. Throughout 2011 twelve 1yr teachers shared their experiences of identity transformation in semi-structured interviews with the researcher. Their interview data was analysed through a theatre-based research method, examining how first experiences shape teachers’ future practice and identity. This presentation includes excerpts from the theatre-based research performance ‘The First Time’, and expands on the methodological approaches taken to analyse the data in a way that reflects the fluid and unpredictable nature of teachers’ identity formation and transformation. This qualitative study allows categories of description to emerge from the data rather than pre-determining categories of investigation. As such the processes of scripting, rehearsing, and performing, were utilised to analyse and re-present the data. In an aim to uncover questions that have been buried by answers, the research is oriented as a phenomenographic inquiry. This mode of inquiry seeks to describe, analyse, and understand the qualitatively different experiences 1yr teachers undergo in their identity formation and transformation. The results of this research reveal that beginning teachers’ identity transformation through their first experiences have both individual features specific to each teacher’s roles and aspirations, and extra-individual factors such as interactions, affiliations, and status, which shape their identity. Categories of description that have emerged from the analysis include survival, liminal, and hegemonic discourses, artifacts as symbols of belonging, and the impact of the contractual nature of teaching. Implications of this research focus on the importance for beginning teachers to develop an understanding of the transformative nature of identity in relation to the practice of teaching, to counter the negative preconceptions beginning teachers are told to expect as rites of passage upon entering the profession. The research outcomes have implications for teacher educators and in-service teachers negotiating the waters of an ever-changing profession.

Performing research as a method of analysis

This paper outlines a theatre-based research method undertaken as a means of analysing and representing data from a study into first-year teachers’ identity transformation. It reports on the processes employed, and the attempt by the researcher to bring together complimentary and innovative ways of interpreting interview data. Interview data from twelve teachers throughout their first year of teaching were scripted, rehearsed and performed to ‘expert’ audiences. The written script provided the basis for analysis of the teachers’ identity transformation as seen through their firsts – epiphanic and/or revelatory experiences that marked a moment of transition or transformation. The script served as an effective tool for data analysis, particularly when developing an understanding of the process of data reduction. Initially dissatisfied with the term ‘data reduction’, believing it to be counter-intuitive to ‘reduce’ the participants’ descriptions of their experiences, the process of scripting emphasised the importance of honing in on meaning, and reconciled such concerns by creating a snapshot of each participant’s first as representative of their experiences. Later, the performance revealed more nuanced understandings of the participants’ experiences as viewed through the eyes of the teacher-actors and audiences. The actors selected to represent the experiences of the first-year teachers in the performance were also teachers. The process of casting, and the teacher-actors’ experiences of rehearsing and performing the work ‘The First Time’ over the past two years will also be discussed. This paper concludes with some of the feedback from various audiences of both the performance and the written thesis from arts- and non-arts-based practitioners. Some feedback contends that the theatre-based method was less rigorous than other more ‘traditional’ methods. Such feedback prompts the consideration of weighing the benefits of employing theatre-based research against the risk of alienating some members of the audiences (both of the performance and the page). This paper includes live/digital excerpts of the theatre-based performance ‘The First Time’.