arXiv subjects

Andrew Hunt

Publications and source records attributed to Andrew Hunt.

4 recordsLinked to original sources

arXiv2018

Stable, predictable and training-free operation of superconducting Bi-2212 Rutherford cable racetrack coils at the very high wire current density of more than 1000 A/mm2

High-temperature superconductors (HTS) could enable high-field magnets much stronger than is possible with Nb-Ti and Nb3Sn, but two key limiting factors have so far been the difficulty of achieving high critical current density in long-length conductors, especially in high-current cables, and the danger of quenches out of the superconducting into the normal state. Here we demonstrate stable, reliable and training-quench-free performance of Bi-2212 racetrack coils wound with a 17-strand Rutherford cable fabricated from wires made with nanospray Bi-2212 powder. These multifilament wires are now being delivered in single lengths of more than 1 km with a new record whole-wire critical current density up to 950 A/mm2 at 30 T at 4.2 K. These coils carried up to 8.6 kA while generating a peak field of 3.5 T at 4.2 K, at a wire current density of 1020 A/mm2. Quite different from the unpredictable training performance of Nb-Ti and Nb3Sn magnets, these Bi-2212 magnets showed no training quenches and entered the flux flow state in a stable manner before thermal runaway and quench occurred. Also quite different from Nb-Ti, Nb3Sn, and REBCO magnets for which localized thermal runaways occur at unpredictable locations, the quenches of Bi-2212 magnets consistently occurred in the high field regions over a conductor length greater than one meter. These characteristics make quench detection rather simple, enabling safe protection, and suggest a new paradigm of constructing quench-predictable superconducting magnets from Bi-2212, which is, like Nb-Ti and Nb3Sn, isotropic, round, multifilament, uniform over km lengths and suitable for Rutherford cable use but, unlike them, much more tolerant of the energy disturbances that often lead Nb-based superconducting magnets to premature quench and long training cycles.

cond-mat.supr-con↗

arXiv2017

K-Means Clustering using Tabu Search with Quantized Means

The Tabu Search (TS) metaheuristic has been proposed for K-Means clustering as an alternative to Lloyd's algorithm, which for all its ease of implementation and fast runtime, has the major drawback of being trapped at local optima. While the TS approach can yield superior performance, it involves a high computational complexity. Moreover, the difficulty in parameter selection in the existing TS approach does not make it any more attractive. This paper presents an alternative, low-complexity formulation of the TS optimization procedure for K-Means clustering. This approach does not require many parameter settings. We initially constrain the centers to points in the dataset. We then aim at evolving these centers using a unique neighborhood structure that makes use of gradient information of the objective function. This results in an efficient exploration of the search space, after which the means are refined. The proposed scheme is implemented in MATLAB and tested on four real-world datasets, and it achieves a significant improvement over the existing TS approach in terms of the intra cluster sum of squares and computational time.

cs.LG↗

arXiv2017

Linear classifier design under heteroscedasticity in Linear Discriminant Analysis

Under normality and homoscedasticity assumptions, Linear Discriminant Analysis (LDA) is known to be optimal in terms of minimising the Bayes error for binary classification. In the heteroscedastic case, LDA is not guaranteed to minimise this error. Assuming heteroscedasticity, we derive a linear classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the Bayes error for binary classification. In addition, we also propose a local neighbourhood search (LNS) algorithm to obtain a more robust classifier if the data is known to have a non-normal distribution. We evaluate the proposed classifiers on two artificial and ten real-world datasets that cut across a wide range of application areas including handwriting recognition, medical diagnosis and remote sensing, and then compare our algorithm against existing LDA approaches and other linear classifiers. The GLD is shown to outperform the original LDA procedure in terms of the classification accuracy under heteroscedasticity. While it compares favourably with other existing heteroscedastic LDA approaches, the GLD requires as much as 60 times lower training time on some datasets. Our comparison with the support vector machine (SVM) also shows that, the GLD, together with the LNS, requires as much as 150 times lower training time to achieve an equivalent classification accuracy on some of the datasets. Thus, our algorithms can provide a cheap and reliable option for classification in a lot of expert systems.

cs.LG↗

arXiv2012

Staircase Codes: FEC for 100 Gb/s OTN

Staircase codes, a new class of forward-error-correction (FEC) codes suitable for high-speed optical communications, are introduced. An ITU-T G.709-compatible staircase code with rate R=239/255 is proposed, and FPGA-based simulation results are presented, exhibiting a net coding gain (NCG) of 9.41 dB at an output error rate of 1E-15, an improvement of 0.42 dB relative to the best code from the ITU-T G.975.1 recommendation. An error floor analysis technique is presented, and the proposed code is shown to have an error floor at 4.0E-21.

cs.IT↗