33 research outputs found
Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads
An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses
Temporal patterns of bat activity on the High Plains of Texas
Texas is home to more wind turbines and more bat species than any other state
in the United States. Insectivorous bats provide an important economical ecosystem
service in this region through agricultural pest regulation. Unfortunately, bats can be
impacted negatively by wind turbines, and migratory bat species particularly so. To
understand how bat activity changes throughout the year in western Texas, activity
was monitored through echolocation calls and opportunistic mist-netting efforts over a
period of four years (2012–2015). Peaks in activity were observed from March through
April, and again in September, which coincides with previously documented migratory periods for many species native to the High Plains of Texas. Findings presented
herein suggest that urban habitats are preferred stopover sites for migratory bat species
while traversing arid regions such as those occurring in western Texas. In addition to
human-made structures, urban habitats harbor non-native trees that provide suitable
roost sites, aggregations of insect prey swarming outdoor light sources, and artificial
water sources. It is important to understand bat activity in western Texas, not only for
the benefit of agricultural pest suppression, but also to predict how the expansion of
wind energy may affect bat populations in this region
Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks
Recurrent neural networks (RNNs) are widely used in computational
neuroscience and machine learning applications. In an RNN, each neuron computes
its output as a nonlinear function of its integrated input. While the
importance of RNNs, especially as models of brain processing, is undisputed, it
is also widely acknowledged that the computations in standard RNN models may be
an over-simplification of what real neuronal networks compute. Here, we suggest
that the RNN approach may be made both neurobiologically more plausible and
computationally more powerful by its fusion with Bayesian inference techniques
for nonlinear dynamical systems. In this scheme, we use an RNN as a generative
model of dynamic input caused by the environment, e.g. of speech or kinematics.
Given this generative RNN model, we derive Bayesian update equations that can
decode its output. Critically, these updates define a 'recognizing RNN' (rRNN),
in which neurons compute and exchange prediction and prediction error messages.
The rRNN has several desirable features that a conventional RNN does not have,
for example, fast decoding of dynamic stimuli and robustness to initial
conditions and noise. Furthermore, it implements a predictive coding scheme for
dynamic inputs. We suggest that the Bayesian inversion of recurrent neural
networks may be useful both as a model of brain function and as a machine
learning tool. We illustrate the use of the rRNN by an application to the
online decoding (i.e. recognition) of human kinematics