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

B-spline neural networks based PID controller for Hammerstein systems

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multi-step ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches

Evaluation of the paraphyletic assemblages within Lonchophyllinae, with description of a new tribe and genus

In the past decade, six new species and one new genus have been described in the Lonchophyllinae (Chiroptera: Phyllostomidae), increasing the number of recognized taxa in the subfamily to four genera and 17 species. During this time, three studies, both morphologic and genetic, indicated the genus Lonchophylla was paraphyletic with respect to other genera in the subfamily. Using tissues from museum voucher specimens, including the holotypes of specimens of Xeronycteris vieirai and Lonchophylla pattoni, issues related to the previous paraphyletic assemblages were addressed. A combination of mitochondrial (Cytb), nuclear data (Fgb-I7, TSHB-I2), chromosome diploid and fundamental numbers, and morphologic characters was used to determine whether all species of Lonchophylla share a common ancestor after diverging from other genera in the subfamily. Based on gene sequence data, a basal, monophyletic, statistically supported radiation within the subfamily Lonchophyllinae was observed in all phylogenetic analyses. We conclude that this assemblage merits recognition as a new tribe and genus, and, therefore, present formal descriptions of the genus as Hsunycteris and the tribe as Hsunycterini. Several other issues related to paraphyly within both the genus Hsunycteris and tribe Lonchophyllini were not resolvable at this time, including that the genus Lonchophylla is paraphyletic and Hsunycteris thomasi contains four genetic species. A species in the genus Hsunycteris remains undescribed because it was not possible to determine which of two lineages the type specimen of H. thomasi is actually a member. Until additional genetic and/or morphologic data are available, resolution of all paraphyletic relationships is not possible. Future studies that focus on utilizing morphologic and genetic (both mitochondrial and nuclear) data from the type specimens of species of Lonchophylla and species of Hsunycteris thomasi are needed to resolve these remaining questions

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

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

B-spline neural networks based PID controller for Hammerstein systems

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multi-step ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches