Trajectory computational techniques emphasizing existence, uniqueness, and construction of solutions to boundary problems for ordinary differential equations Final report

Trajectory computational techniques emphasizing existence, uniqueness, and construction of solutions to boundary problems for ordinary differential equation

Neural network fault diagnosis of a trolling motor based on feature reduction techniques for an unmanned surface vehicle

This article presents a novel approach to the diagnosis of unbalanced faults in a trolling motor under stationary operating conditions. The trolling motor being typically of that used as the propulsion system for an unmanned surface vehicle, the diagnosis approach is based on the use of discrete wavelet transforms as a feature extraction tool and a time-delayed neural network for fault classification. The time-delayed neural network classifies between healthy and faulty conditions of the trolling motor by analysing the stator current and vibration. To overcome feature redundancy, which affects diagnosis accuracy, several feature reduction methods have been tested, and the orthogonal fuzzy neighbourhood discriminant analysis approach is found to be the most effective method. Four faulty conditions were analysed under laboratory conditions, where one of the blades causing damage to the trolling motor is cut into 10%, 25%, half and then into full to simulate the effects of propeller blades being damaged partly or fully. The results obtained from the real-time simulation demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and accurately

Reconstruction of deglacial sea surface temperatures in the tropical Pacific from selective analysis of a fossil coral

The Sr/Ca of coral skeletons demonstrates potential as an indicator of sea surface temperatures (SSTs). However, the glacial-interglacial SST ranges predicted from Sr/Ca of fossil corals are usually higher than from other marine proxies. We observed infilling of secondary aragonite, characterised by high Sr/Ca ratios, along intraskeletal pores of a fossil coral from Papua New Guinea that grew during the penultimate deglaciation (130 +/- 2 ka). Selective microanalysis of unaltered areas of the fossil coral indicates that SSTs at similar to 130 ka were &lt;= 1 degrees C cooler than at present in contrast with bulk measurements ( combining infilled and unaltered areas) which indicate a difference of 6-7 degrees C. The analysis of unaltered areas of fossil skeletons by microprobe techniques may offer a route to more accurate reconstruction of past SSTs.</p

A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

Rolling element bearing defects are among the main reasons for the breakdown of electrical machines, and therefore, early diagnosis of these is necessary to avoid more catastrophic failure consequences. This paper presents a novel approach for identifying rolling element bearing defects in brushless DC motors under non-stationary operating conditions. Stator current and lateral vibration measurements are selected as fault indicators to extract meaningful features, using a discrete wavelet transform. These features are further reduced via the application of orthogonal fuzzy neighbourhood discriminative analysis. A recurrent neural network is then used to detect and classify the presence of bearing faults. The proposed system is implemented and tested in simulation on data collected from an experimental setup, to verify its effectiveness and reliability in accurately detecting and classifying the various faults

Large-Scale Image Processing with the ROTSE Pipeline for Follow-Up of Gravitational Wave Events

Electromagnetic (EM) observations of gravitational-wave (GW) sources would bring unique insights into a source which are not available from either channel alone. However EM follow-up of GW events presents new challenges. GW events will have large sky error regions, on the order of 10-100 square degrees, which can be made up of many disjoint patches. When searching such large areas there is potential contamination by EM transients unrelated to the GW event. Furthermore, the characteristics of possible EM counterparts to GW events are also uncertain. It is therefore desirable to be able to assess the statistical significance of a candidate EM counterpart, which can only be done by performing background studies of large data sets. Current image processing pipelines such as that used by ROTSE are not usually optimised for large-scale processing. We have automated the ROTSE image analysis, and supplemented it with a post-processing unit for candidate validation and classification. We also propose a simple ad hoc statistic for ranking candidates as more likely to be associated with the GW trigger. We demonstrate the performance of the automated pipeline and ranking statistic using archival ROTSE data. EM candidates from a randomly selected set of images are compared to a background estimated from the analysis of 102 additional sets of archival images. The pipeline's detection efficiency is computed empirically by re-analysis of the images after adding simulated optical transients that follow typical light curves for gamma-ray burst afterglows and kilonovae. We show that the automated pipeline rejects most background events and is sensitive to simulated transients to limiting magnitudes consistent with the limiting magnitude of the images

A Mechanism for Ferrimagnetism and Incommensurability in One-Dimensional Systems

A mechanism for ferrimagnetism in (1+1)-dimensions is discussed. The ferrimagnetism is cased by interactions described by operators with non-zero conformal spin. Such interactions appear in such problems as the problem of tunneling between Luttinger liquids and the problem of frustrated spin ladder. I present exact solutions for a representative class of models containing such interactions together with a simple mean field analysis. It is shown that the interactions (i) dynamically generate static oscillations with a wave vector dependent on the coupling constant, (ii) give rise to a finite magnetic moment at $T = 0$ accompanied by the soft mode with a non-relativistic ({\it ferromagnetic}) dispersion $E \sim k^2$, (iii) generate massive (roton) modes.Comment: replaced by the extended version, references adde

An interval Kalman filter-based fuzzy multi-sensor fusion approach for fault-tolerant heading estimation of an autonomous surface vehicle

This article presents a novel fuzzy–logic based multi-sensor data fusion algorithm for combining heading estimates from three separate weighted interval Kalman filters to construct a robust, fault-tolerant heading estimator for the navigation of the Springer autonomous surface vehicle. A single, low-cost gyroscopic unit and three independent compasses are used to acquire data onboard the vehicle. The gyroscope data, prone to sporadic bias drifts, are fused individually with readings from each of the compasses via a weighted interval Kalman filter. Unlike the standard Kalman filter, the weighted interval Kalman filter is able to provide a robust heading estimate even when subject to such gyroscope bias drifts. The three ensuing weighted interval Kalman filter estimates of the vehicle’s heading are then fused via a fuzzy logic algorithm designed to provide an accurate heading estimate even when two of the three compasses develop a fault at any time. Simulations and real-time trials demonstrate the effectiveness of the proposed method. </jats:p