Caged birds and cloning machines: how student imagery 'speaks' to us about cultures of schooling and student participation

With the advent of the United Nations Convention on the Rights of the Child (CRC), there is an increasing requirement that schools ensure children and young people's views are voiced, listened to and taken seriously on matters of significance. Encouraging these shifts by law is one thing; changing the culture in schools is another. For a significant proportion of schools, actively engaging students' voices on how they experience education poses a significant challenge and crucial gaps may exist between the rhetoric espoused and a school's readiness for genuine student involvement. This ethnographic study illuminates tensions that persist between headteachers' espoused views of how students are valued and students' creative images of their actual post-primary schooling experience. If cultures of schooling are to nurture the true spirit of democratic pupil participation implied by changes in the law, there is a need to develop genuine processes of student engagement in which students and staff can collaborate towards greater shared understandings of a school's priorities.

HTS Machines as Enabling Technology for All-Electric Airborne Vehicles

Environmental protection has now become paramount as evidence mounts to support the thesis of human activity-driven global warming. A global reduction of the emissions of pollutants into the atmosphere is therefore needed and new technologies have to be considered. A large part of the emissions come from transportation vehicles, including cars, trucks and airplanes, due to the nature of their combustion-based propulsion systems. Our team has been working for several years on the development of high power density superconducting motors for aircraft propulsion and fuel cell based power systems for aircraft. This paper investigates the feasibility of all-electric aircraft based on currently available technology. Electric propulsion would require the development of high power density electric propulsion motors, generators, power management and distribution systems. The requirements in terms of weight and volume of these components cannot be achieved with conventional technologies; however, the use of superconductors associated with hydrogen-based power plants makes possible the design of a reasonably light power system and would therefore enable the development of all-electric aero-vehicles. A system sizing has been performed both for actuators and for primary propulsion. Many advantages would come from electrical propulsion such as better controllability of the propulsion, higher efficiency, higher availability and less maintenance needs. Superconducting machines may very well be the enabling technology for all-electric aircraft development.

Improved conjugate gradient implementation for least squares support vector machines

As a promising method for pattern recognition and function estimation, least squares support vector machines (LS-SVM) express the training in terms of solving a linear system instead of a quadratic programming problem as for conventional support vector machines (SVM). In this paper, by using the information provided by the equality constraint, we transform the minimization problem with a single equality constraint in LS-SVM into an unconstrained minimization problem, then propose reduced formulations for LS-SVM. By introducing this transformation, the times of using conjugate gradient (CG) method, which is a greatly time-consuming step in obtaining the numerical solution, are reduced to one instead of two as proposed by Suykens et al. (1999). The comparison on computational speed of our method with the CG method proposed by Suykens et al. and the first order and second order SMO methods on several benchmark data sets shows a reduction of training time by up to 44%. (C) 2011 Elsevier B.V. All rights reserved.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.