Maximum likelihood analysis of multi-stress accelerated life test data of series systems with competing log-normal causes of failure

This article presents frequentist inference of accelerated life test data of series systems with independent log-normal component lifetimes. The means of the component log-lifetimes are assumed to depend on the stress variables through a linear stress translation function that can accommodate the standard stress translation functions in the literature. An expectation-maximization algorithm is developed to obtain the maximum likelihood estimates of model parameters. The maximum likelihood estimates are then further refined by bootstrap, which is also used to infer about the component and system reliability metrics at usage stresses. The developed methodology is illustrated by analyzing a real as well as a simulated dataset. A simulation study is also carried out to judge the effectiveness of the bootstrap. It is found that in this model, application of bootstrap results in significant improvement over the simple maximum likelihood estimates.

Robust Whisper Activity Detection Using Long-Term Log Energy Variation of Sub-Band Signal

The goal in the whisper activity detection (WAD) is to find the whispered speech segments in a given noisy recording of whispered speech. Since whispering lacks the periodic glottal excitation, it resembles an unvoiced speech. This noise-like nature of the whispered speech makes WAD a more challenging task compared to a typical voice activity detection (VAD) problem. In this paper, we propose a feature based on the long term variation of the logarithm of the short-time sub-band signal energy for WAD. We also propose an automatic sub-band selection algorithm to maximally discriminate noisy whisper from noise. Experiments with eight noise types in four different signal-to-noise ratio (SNR) conditions show that, for most of the noises, the performance of the proposed WAD scheme is significantly better than that of the existing VAD schemes and whisper detection schemes when used for WAD.

Thermodynamical versus log-Poisson distribution in turbulence

The thermodynamical model of intermittency in fully developed turbulence due to Castaing (B. Castaing, J. Phys. II France 6 (1996) 105) is investigated and compared with the log-Poisson model (Z-S, She, E. Leveque, Phys. Rev. Lett. 72 (1994) 336). It is shown that the thermodynamical model obeys general scaling laws and corresponds to the degenerate class of scale-invariant statistics. We also find that its structure function shapes have physical behaviors similar to the log-Poisson's one. The only difference between them lies in the convergence of the log-Poisson's structure functions and divergence of the thermodynamical one. As far as the comparison with experiments on intermittency is concerned, they are indifferent.

An analysis of migratory salmonid catch effort data, derived from anglers' log books, 1991

1000 log books were issued to anglers of which 236 were returned, those from the rivers Derwent, Kent, Lune and Ribble accounted for the vast majority. The Derwent had the highest catch rate of these rivers: one salmon every 13.89 hours followed by the Lune, Kent and Ribble at 16.39, 18.87 and 35.71 hours, respectively. For sea trout the Lune, Derwent and Ribble had a catch rate of approximately one fish every 10.0 hours (9.8, 10.0 and 10.64 hours),and for the Kent one fish per 16.1 hours fished. Salmon angling visits were, in general,longer than those for sea trout being between 2 and 6 hours as opposed to 2 to 4 hours. On the majority of visits (>80%) no fish were caught and was the same for salmon and sea trout. For salmon the majority of fish were caught on fly, spinner or worm, and the least on prawn. For sea trout fly predominated. The majority of salmon caught were less than 91b in weight and were presumed to be grilse (1 sea winter). The majority of the sea trout caught weighed between 1 and 31b. The pattern of catch, effort, CPUE, abundance and catchability for salmon and sea trout were modelled using the data from the rivers Derwent, Kent and Lune. Flow significantly influenced catch, effort and catchability of salmon which had entered in a particular month. For sea trout flow was not significantly correlated with any of the dependent variables. The catchability coefficient for salmon, determined from the total number of fish, remained relatively constant over the period June to October indicating that CPUE was a reasonable measure of within season abundance. This was not found to be the case for sea trout.

Development of a fishing log for use in trawlers

The paper describes the technical and operational features of a composite equipment for simultaneous measurement of five important parameters, warp load, boat speed, water temperature, water salinity and air temperature pertaining to the craft, gear and the environment. The equipment is designed for continuous measurement in small and medium crafts easily without disturbance to routine fishing operations. The system operated on 9V supply, is suitable for portable operations from one vessel to another. The compact electronic meter kept in the wheel-house displays the data one by one in engineering units.

Strengthening the log book recording system for fishing vessels: Sri Lanka

Activities of the project included: preparation of awareness materials on data reporting; organizing stakeholder awareness programmes; setting and maintaining an electronic database; inputs from participants and recommendations

Kernelized log linear models for continuous speech recognition

Large margin criteria and discriminative models are two effective improvements for HMM-based speech recognition. This paper proposed a large margin trained log linear model with kernels for CSR. To avoid explicitly computing in the high dimensional feature space and to achieve the nonlinear decision boundaries, a kernel based training and decoding framework is proposed in this work. To make the system robust to noise a kernel adaptation scheme is also presented. Previous work in this area is extended in two directions. First, most kernels for CSR focus on measuring the similarity between two observation sequences. The proposed joint kernels defined a similarity between two observation-label sequence pairs on the sentence level. Second, this paper addresses how to efficiently employ kernels in large margin training and decoding with lattices. To the best of our knowledge, this is the first attempt at using large margin kernel-based log linear models for CSR. The model is evaluated on a noise corrupted continuous digit task: AURORA 2.0. © 2013 IEEE.

Classification using log Gaussian Cox processes

McCullagh and Yang (2006) suggest a family of classification algorithms based on Cox processes. We further investigate the log Gaussian variant which has a number of appealing properties. Conditioned on the covariates, the distribution over labels is given by a type of conditional Markov random field. In the supervised case, computation of the predictive probability of a single test point scales linearly with the number of training points and the multiclass generalization is straightforward. We show new links between the supervised method and classical nonparametric methods. We give a detailed analysis of the pairwise graph representable Markov random field, which we use to extend the model to semi-supervised learning problems, and propose an inference method based on graph min-cuts. We give the first experimental analysis on supervised and semi-supervised datasets and show good empirical performance.

Estimating Sensor Motion from Wide-Field Optical Flow on a Log-Dipolar Sensor

Log-polar image architectures, motivated by the structure of the human visual field, have long been investigated in computer vision for use in estimating motion parameters from an optical flow vector field. Practical problems with this approach have been: (i) dependence on assumed alignment of the visual and motion axes; (ii) sensitivity to occlusion form moving and stationary objects in the central visual field, where much of the numerical sensitivity is concentrated; and (iii) inaccuracy of the log-polar architecture (which is an approximation to the central 20°) for wide-field biological vision. In the present paper, we show that an algorithm based on generalization of the log-polar architecture; termed the log-dipolar sensor, provides a large improvement in performance relative to the usual log-polar sampling. Specifically, our algorithm: (i) is tolerant of large misalignmnet of the optical and motion axes; (ii) is insensitive to significant occlusion by objects of unknown motion; and (iii) represents a more correct analogy to the wide-field structure of human vision. Using the Helmholtz-Hodge decomposition to estimate the optical flow vector field on a log-dipolar sensor, we demonstrate these advantages, using synthetic optical flow maps as well as natural image sequences.