Journal Bearings

A plurality of bearing sectors are mounted in a housing. Each sector functions as a lobed area in the bearing to obtain the required lubricant film geometry

A comparative study of the physiological properties of the inner ear in Doppler shift compensating bats (Rhinolophus rouxi and Pteronotus parnellit)

Cochlear microphonic (CM) and evoked neural (N-1) potentials were studied in two species of Doppler shift compensating bats with the aid of electrodes chronically implanted in the scala tympani. Potentials were recorded from animals fully recovered from the effects of anesthesia and surgery. InPteronotus p. parnellii andRhinolophus rouxi the CM amplitude showed a narrow band, high amplitude peak at a frequency about 200 Hz above the resting frequency of each species. InPteronotus the peak was 25–35 dB higher in amplitude than the general CM level below or above the frequency of the amplitude peak. InRhinolophus the amplitude peak was only a few dB above the general CM level but it was prominent because of a sharp null in a narrow band of frequencies just below the peak. The amplitude peak and the null were markedly affected by body temperature and anesthesia. InPteronotus high amplitude CM potentials were produced by resonance, and stimulated cochlear emissions were prominent inPteronotus but they were not observed inRhinolophus. InPteronotus the resonance was indicated by a CM afterpotential that occurred after brief tone pulses. The resonance was not affected by the addition of a terminal FM to the stimulus and when the ear was stimulated with broadband noise it resulted in a continual state of resonance. Rapid, 180 degree phase shifts in the CM were observed when the stimulus frequency swept through the frequency of the CM amplitude peak inPteronotus and the frequency of the CM null inRhinolophus. These data indicate marked differences in the physiological properties of the cochlea and in the mechanisms responsible for sharp tuning in these two species of bats

A deep matrix factorization method for learning attribute representations

Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies can not interpret. In this work we propose a novel model, Deep Semi-NMF, that is able to learn such hidden representations that allow themselves to an interpretation of clustering according to different, unknown attributes of a given dataset. We also present a semi-supervised version of the algorithm, named Deep WSF, that allows the use of (partial) prior information for each of the known attributes of a dataset, that allows the model to be used on datasets with mixed attribute knowledge. Finally, we show that our models are able to learn low-dimensional representations that are better suited for clustering, but also classification, outperforming Semi-Non-negative Matrix Factorization, but also other state-of-the-art methodologies variants.Comment: Submitted to TPAMI (16-Mar-2015

Operation of hydrodynamic journal bearings in sodium at temperatures to 800 deg F and speeds to 12000 rpm

Operation of hydrodynamic journal bearings in liquid sodium at high temperatures and high speed

Learning Audio Sequence Representations for Acoustic Event Classification

Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC