Optimization of long rural feeders using a genetic algorithm

This paper describes the optimization of conductor size and the voltage regulator location & magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.

Service learning in teacher education

If the student wellbeing pedagogy characterised by the troika metaphor is to become more widely adopted, beginning teachers need to be inducted into service learning. In this chapter, we discuss the implementation and outcomes of a service learning program in a Bachelor of Education course in Australia. The program provides pre-service teachers with insights into service learning practice. Pre-service teachers are given supported opportunities to examine and challenge traditional beliefs and values about student diversity and the role of schools in developing a more inclusive society. They are supported in developing ethics of care and concern for inclusive and equitable practices – characteristics necessary for quality teaching. Thus, the Queensland University of Technology (QUT) service learning program is an ideal example of the troika effect in practice, in that the pedagogy fuses values education, quality teaching and service learning to develop within each student an inclusive ethical framework that will inform their classroom practice as beginning quality teachers.

Colin Marsh (2008) Becoming a Teacher: Knowledge, Skills and Issues. 4th edition, Frenchs Forrest: Pearson Education Australia, 460 pages

Becoming a Teacher is structured in five very readable sections. The introductory section addresses the nature of teaching and the importance of developing a sense of purpose for teaching in a 21st century classroom. It also introduces some key concepts that are explored throughout the volume according to the particular chapter focus of each part. For example, the chapters in Part 2 explore aspects of student learning and the learning environment and focus on how students develop and learn, learner motivation, developing self esteem and learning environments. The concepts developed in this section, such as human development, stages of learning, motivation, and self-concept are contextualised in terms of theories of cognitive development and theories of social, emotional and moral development. The author, Colin Marsh, draws on his extensive experience as an educator to structure the narrative of chapters in this part via checklists for observation, summary tables, sample strategies for teaching at specific stages of student development, and questions under the heading ‘your turn’. Case studies such as ‘How I use Piaget in my teaching’ make that essential link between theory and practice, something which pre-service teachers struggle with in the early phases of their university course. I was pleased to see that Marsh also explores the contentious and debated aspects of these theoretical frameworks to demonstrate that pre-service teachers must engage with and critique the ways in which theories about teaching and learning are applied. Marsh weaves in key quotations and important references into each chapter’s narrative and concludes every chapter with summary comments, reflection activities, lists of important references and useful web sources. As one would expect of a book published in 2008, Becoming a Teacher is informed by the most recent reports of classroom practice, current policy initiatives and research.

Blackboard as an online learning environment : what do teacher education students and staff think?

Abstract: As online learning environments now have an established presence in higher education we need to ask the question: How effective are these environments for student learning? Online environments can provide a different type of learning experience than traditional face-to-face contexts (for on-campus students) or print-based materials (for distance learners). This article identifies teacher education student and staff perceptions of teaching and learning using the online learning management system, Blackboard. Perceptions of staff and students are compared and implications for teacher education staff interested in providing high quality learning environments within an online space are discussed.

Enhancing professional ethics : service-learning in teacher education

The ethical conduct of professionals has been the focus of increasing scrutiny over the past several decades as members of the public, the media, professional bodies, and legislative authorities have struggled to define ethical behaviour in times of governmental change, increasing internationalisation, globalised communications, threats of terrorism, and the challenges of developments in science and medicine (e.g., Demmke & Bossaert, 2004). National governments and transnational bodies have responded to these concerns about ethics and corruption through measures such as the United Nations Convention Against Corruption (United Nations Office on Drugs and Crime, 2004), Transparency International’s annual corruption index (2010) and Queensland’s Public Sector Ethics Act 1994 (Queensland Parliament 1994). Similarly, academic interest in ethics and its application across a range of domains(e.g., business, health care, social welfare, criminal justice, law, journalism, defence, environment, and media) has also increased. To illustrate, in 1993, a non-partisan, non-profit national umbrella organisation, the Australian Association for Professional and Applied Ethics, was formed following a conference concerned with the teaching of ethics (http://www.arts.unsw.edu.au./aapae/about_aapae/about_aapae.htm), while a recent review of the Excellence in Research for Australian rankings of national and international academic journals revealed that 16 journals related to ethics had received the top ratings of A* or A (Australian Research Council, 2009). In this chapter we examine professional ethics and argue, with specific reference to the context of pre-service teacher education, that Service-learning is one way of enhancing emerging professionals’ understanding of ethics.

Shrinking neighborhood evolution : a novel stochastic algorithm for numerical optimization

Focusing on the conditions that an optimization problem may comply with, the so-called convergence conditions have been proposed and sequentially a stochastic optimization algorithm named as DSZ algorithm is presented in order to deal with both unconstrained and constrained optimizations. The principle is discussed in the theoretical model of DSZ algorithm, from which we present the practical model of DSZ algorithm. Practical model efficiency is demonstrated by the comparison with the similar algorithms such as Enhanced simulated annealing (ESA), Monte Carlo simulated annealing (MCS), Sniffer Global Optimization (SGO), Directed Tabu Search (DTS), and Genetic Algorithm (GA), using a set of well-known unconstrained and constrained optimization test cases. Meanwhile, further attention goes to the strategies how to optimize the high-dimensional unconstrained problem using DSZ algorithm.

Defining and mapping teacher practice in technology classrooms

The indecision surrounding the definition of Technology extends to the classroom as not knowing what a subject “is” affects how it is taught. Similarly, its relative newness – and consequent lack of habitus in school settings - means that it is still struggling to find its own place in the curriculum as well as resolve its relationship with more established subject domains, particularly Science and Mathematics. The guidance from syllabus documents points to open-ended student-directed projects where extant studies indicate a more common experience of teacher –directed activities and an emphasis on product over process. There are issues too for researchers in documenting classroom observations and in analysing teacher practice in new learning environments. This paper presents a framework for defining and mapping classroom practice and for attempting to describe the social practice in the Technology classroom. The framework is a bricolage which draws on contemporary research. More formally, the development of the framework is consonant with the aim of design-based research to develop a flexible, adaptive and generalisable theory to better understanding a teaching domain where promise is not seen to match current reality. The framework may also inform emergent approaches to STEM (Science, Technology, Education and Mathematics) in education.

Self-authorship in child care student teachers

Research related to personal epistemology in teacher education indicates that teachers’ beliefs about knowing and learning influence their pedagogical practices. In the current study, we interviewed 31 child care students to investigate the relationship between personal epistemology and beliefs about children’s learning as they engaged in teaching practices with young children. We drew on self authorship theory to analyze this data, which considers the evolving capacity of learners to analyze and make informed judgments about knowledge (personal epistemology)in the light of their professional identity (intrapersonal beliefs) and interdependent social relationships (interpersonal beliefs). The majority of students described practical personal epistemologies which involved either modeling, reflection on, or evaluation of practical strategies. These epistemologies have implications for child care teachers’ professional identities and their relationships with families, children, and staff in child care contexts.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

A hardware-based multi-disciplinary design optimization method for aeronautical applications

There are many applications in aeronautics where there exist strong couplings between disciplines. One practical example is within the context of Unmanned Aerial Vehicle(UAV) automation where there exists strong coupling between operation constraints, aerodynamics, vehicle dynamics, mission and path planning. UAV path planning can be done either online or offline. The current state of path planning optimisation online UAVs with high performance computation is not at the same level as its ground-based offline optimizer's counterpart, this is mainly due to the volume, power and weight limitations on the UAV; some small UAVs do not have the computational power needed for some optimisation and path planning task. In this paper, we describe an optimisation method which can be applied to Multi-disciplinary Design Optimisation problems and UAV path planning problems. Hardware-based design optimisation techniques are used. The power and physical limitations of UAV, which may not be a problem in PC-based solutions, can be approached by utilizing a Field Programmable Gate Array (FPGA) as an algorithm accelerator. The inevitable latency produced by the iterative process of an Evolutionary Algorithm (EA) is concealed by exploiting the parallelism component within the dataflow paradigm of the EA on an FPGA architecture. Results compare software PC-based solutions and the hardware-based solutions for benchmark mathematical problems as well as a simple real world engineering problem. Results also indicate the practicality of the method which can be used for more complex single and multi objective coupled problems in aeronautical applications.

Closing the gap between bandit and full-information online optimization : high-probability regret bound

We demonstrate a modification of the algorithm of Dani et al for the online linear optimization problem in the bandit setting, which allows us to achieve an O( \sqrt{T ln T} ) regret bound in high probability against an adaptive adversary, as opposed to the in expectation result against an oblivious adversary of Dani et al. We obtain the same dependence on the dimension as that exhibited by Dani et al. The results of this paper rest firmly on those of Dani et al and the remarkable technique of Auer et al for obtaining high-probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.

Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.