A Videogrammetric As-Built Data Collection Method for Digital Fabrication of Sheet Metal Roof Panels

A roofing contractor typically needs to acquire as-built dimensions of a roof structure several times over the course of its build to be able to digitally fabricate sheet metal roof panels. Obtaining these measurements using the exiting roof surveying methods could be costly in terms of equipment, labor, and/or worker exposure to safety hazards. This paper presents a video-based surveying technology as an alternative method which is simple to use, automated, less expensive, and safe. When using this method, the contractor collects video streams with a calibrated stereo camera set. Unique visual characteristics of scenes from a roof structure are then used in the processing step to automatically extract as-built dimensions of roof planes. These dimensions are finally represented in a XML format to be loaded into sheet metal folding and cutting machines. The proposed method has been tested for a roofing project and the preliminary results indicate its capabilities.

Vowel normalisation: Time-domain processing of the internal dynamics of speech

Human listeners can identify vowels regardless of speaker size, although the sound waves for an adult and a child speaking the ’same’ vowel would differ enormously. The differences are mainly due to the differences in vocal tract length (VTL) and glottal pulse rate (GPR) which are both related to body size. Automatic speech recognition machines are notoriously bad at understanding children if they have been trained on the speech of an adult. In this paper, we propose that the auditory system adapts its analysis of speech sounds, dynamically and automatically to the GPR and VTL of the speaker on a syllable-to-syllable basis. We illustrate how this rapid adaptation might be performed with the aid of a computational version of the auditory image model, and we propose that an auditory preprocessor of this form would improve the robustness of speech recognisers.

Cortical substrates for exploratory decisions in humans.

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.

Design and Performance Analysis of a 2.5 MW-Class HTS Synchronous Motor for Ship Propulsion

The development of cryogenic technology and high temperature superconducting (HTS) materials has seen continued interest worldwide in the development of HTS machines since the late 1980s. In this paper, the authors present a conceptual design of a 2.5 MW class synchronous motor. The structure of the motor is specified and the motor performance is analyzed via a three-dimensional model using the finite element method (FEM). Rotor optimization is carried out to decrease the harmonic components in the air gap field generated by HTS tapes. Based on the results of this 3D simulation, the determination of the operating conditions and load angle is discussed with consideration to the HTS material properties. The economic viability of air-core and iron-core designs is compared. The results show that this type of HTS machine has the potential to achieve an economic, efficient and effective machine design, which operates at a low load angle, and this design process provides a practical way to simulate and analyze the performance of such machines.

Analysis of fields in an air-cored superconducting synchronous motor with an HTS racetrack field winding

High temperature superconducting (HTS) synchronous motors can offer significant weight and size reductions, as well as improved efficiency, over conventional copper-wound machines due to the higher current density of high temperature superconducting (HTS) materials. In order to optimise the design parameters and performance of such a machine, this paper proposes a basic physical model of an air-cored HTS synchronous motor with a copper armature winding and HTS field winding. An analytical method for the field analysis in the synchronous motor is then presented, followed by a numerical finite element analysis (FEA) model to verify the analytical solution. The model is utilised to study the influence of the geometry of the HTS coils on the magnetic field at the armature winding, and geometrical parameter optimisation is carried out using this theoretical model to obtain a more sinusoidal magnetic field at the armature, which has a major influence on the performance of the motor.

Antiwindup design for induction motor control in the field weakening domain

Operation of induction machines in the high-speed and/or high-torque range requires field-weakening to comply with voltage and current physical limitations. This paper presents an anti-windup approach to this problem: rather than developing an ad-hoc field weakening strategy in the high-speed region, we equip an unconstrained vector-control design with an anti-windup module that automatically adjusts the current and flux set-points so that voltage and current constraints are satisfied at every operating point. The anti-windup module includes a feedforward modification of the set point aimed at maximizing the available torque in steady-state and a feedback modification of the controller based on an internal model-based antiwindup scheme. This paper includes a complete stability analysis of the proposed solution and presents encouraging experimental results on an industrial drive. © 2012 IEEE.

Anisotropy of 2G HTS racetrack coils in external magnetic fields

Pancake or racetrack coils wound with second generation high-temperature superconductors (2G HTSs) are important elements for numerous applications of HTS. The applications of these coils are primarily in rotating machines such as motors and generators where they must withstand external magnetic fields from various orientations. The characterization of 2G HTS coils is mostly focused on AC loss assessment, critical current and maximum magnetic field evaluation. In this study, racetrack coils will be placed in different orientations of external magnetic fields - Jc (Ic) versus angle measurements will be performed and interpreted. Full attention is paid to studies of anisotropy Jc versus angle curves for short samples of 2G HTS tapes. As will be shown, the shape of the Jc versus angle curves for tapes has a strong influence on the Jc (Ic) versus angle curves for coils. In this work, a unique and unpredicted behavior of the Jc versus angle curves for the 2G HTS racetrack coils was found. This will be analyzed and fully explained. © 2013 IOP Publishing Ltd.

Bayesian Structured Prediction Using Gaussian Processes

We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M3N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct.

Sensorless commutation of a two-phase linear machine for marine renewable power generation

A sensorless scheme is presented for a two-phase permanent-magnet linear machine targeted for use in marine wave-power generation. This is a field where system reliability is a key concern. The scheme is able to extract the effective inductance and back-emf of the machine's phases simultaneously from measurements of the current ripple present on the power electronic converter. These measurements can then be used to estimate position. An enhancement to the scheme in the presence of spatially-varying mutual inductance between phases allows more accurate and reliable tracking from indutance-based measurements than would otherwise be expected. This scheme is able to operate at any speed including, critically, when stationary. Experimental results show promise for the scheme, although some work to reduce the level of noise would be desirable. © 2013 IEEE.

Design and market considerations for axial flux superconducting electric machine design

In this paper, the authors investigate a number of design and market considerations for an axial flux superconducting electric machine design that uses high temperature superconductors. The axial flux machine design is assumed to utilise high temperature superconductors in both wire (stator winding) and bulk (rotor field) forms, to operate over a temperature range of 65-77 K, and to have a power output in the range from 10s of kW up to 1 MW (typical for axial flux machines), with approximately 2-3 T as the peak trapped field in the bulk superconductors. The authors firstly investigate the applicability of this type of machine as a generator in small- and medium-sized wind turbines, including the current and forecasted market and pricing for conventional turbines. Next, a study is also carried out on the machine's applicability as an in-wheel hub motor for electric vehicles. Some recommendations for future applications are made based on the outcome of these two studies. Finally, the cost of YBCO-based superconducting (2G HTS) wire is analysed with respect to competing wire technologies and compared with current conventional material costs and current wire costs for both 1G and 2G HTS are still too great to be economically feasible for such superconducting devices.

Notch and strain rate sensitivity of non-crimp fabric composites

The notch and strain rate sensitivity of non-crimp glass fibre/vinyl-ester laminates subjected to uniaxial tensile loads has been investigated experimentally. Two sets of notch configurations were tested; one where circular holes were drilled and another where fragment simulating projectiles were fired through the plate creating a notch. Experiments were conducted for strain rates ranging from 10-4 s-1 to 102 s-1 using servo hydraulic machines. A significant increase in strength with increasing strain rate was observed for both notched and un-notched specimens. High speed photography revealed changes in failure mode, for certain laminate configurations, as the strain rate increased. The tested laminate configurations showed fairly small notch sensitivity for the whole range of strain rates. © 2008 Elsevier Ltd. All rights reserved.

Notch and strain rate sensitivity of non crimp fabric composites

The notch and strain rate sensitivity of non-crimp glass fibre/vinyl-ester laminates subjected to uniaxial tensile loads has been investigated experimentally. Two set of notch configurations were tested; one where circular holes were drilled and another where fragment simulating projectiles were fired through the plate creating a notch. Experiments were conducted for strain rates ranging from 10-4/s to 102/s using servo hydraulic machines. A significant increase in strength with increasing strain rate was observed for both notched and unnotched specimens. High speed photography revealed changes in failure mode, for certain laminate configurations, as the strain rate increased. The tested laminate configurations showed fairly small notch sensitivity for the whole range of strain rates.

Investigation of magnetic wedge effects in large-scale BDFMs

This paper studies the effects of magnetic wedges on the equivalent circuit parameters of the Brushless Doubly-Fed Machine (BDFM). Magnetic wedges are used in slot openings of large electrical machines to reduce magnetizing currents, but the study of their effects on the BDFM performance is not straightforward due to the complex magnetic fields in the BDFM. Equivalent circuit and FE models have been developed for a 250 kW BDFM taking into account the effects of wedges and verified experimentally.

Design and performance analysis of a 6 MW medium-speed Brushless DFIG

The paper presents the design and performance analysis of a 6 MW medium-speed Brushless Doubly-Fed Induction Generation (Brushless DFIG) for a wind turbine drivetrain. Two machines with different frame sizes have been designed to show the flexibility of the design procedure. The mediumspeed Brushless DFIG in combination with a two stage gearbox offers a low-cost, low-maintenance and reliable drivetrain for wind turbine applications.

The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.