643 research outputs found
Phase Open Fault Tolerant Control of High Reliability Doubly-Salient Wound-Field Machine
Doubly Salient Wound-Field Machine (DSWFM) can be employed on aeronautics starter-generator because it has good performance on both power generation and starting. To improve the system reliability, a three-phase four bridge legs converter which has fault tolerant capability is proposed to solve one phase open-circuit fault problem of the DSWFM. And the advantage of the proposed converter to the full-bridge converter fault-tolerant mode is analyzed. With the study of DSWFM theory and torque equation, a constant torque fault-tolerant strategy is proposed to keep the performance and reduce the torque ripple. The drive system after fault identification can be reconstructed by the proposed method, and the machine performance can recover quickly. Simulations confirm the feasibility of the proposed fault tolerant system
Low Rank Approximation of Binary Matrices: Column Subset Selection and Generalizations
Low rank matrix approximation is an important tool in machine learning. Given
a data matrix, low rank approximation helps to find factors, patterns and
provides concise representations for the data. Research on low rank
approximation usually focus on real matrices. However, in many applications
data are binary (categorical) rather than continuous. This leads to the problem
of low rank approximation of binary matrix. Here we are given a
binary matrix and a small integer . The goal is to find two binary
matrices and of sizes and respectively, so
that the Frobenius norm of is minimized. There are two models of this
problem, depending on the definition of the dot product of binary vectors: The
model and the Boolean semiring model. Unlike low rank
approximation of real matrix which can be efficiently solved by Singular Value
Decomposition, approximation of binary matrix is -hard even for .
In this paper, we consider the problem of Column Subset Selection (CSS), in
which one low rank matrix must be formed by columns of the data matrix. We
characterize the approximation ratio of CSS for binary matrices. For
model, we show the approximation ratio of CSS is bounded by
and this bound is asymptotically tight. For
Boolean model, it turns out that CSS is no longer sufficient to obtain a bound.
We then develop a Generalized CSS (GCSS) procedure in which the columns of one
low rank matrix are generated from Boolean formulas operating bitwise on
columns of the data matrix. We show the approximation ratio of GCSS is bounded
by , and the exponential dependency on is inherent.Comment: 38 page
Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli
In the real world, visual stimuli received by the biological visual system
are predominantly dynamic rather than static. A better understanding of how the
visual cortex represents movie stimuli could provide deeper insight into the
information processing mechanisms of the visual system. Although some progress
has been made in modeling neural responses to natural movies with deep neural
networks, the visual representations of static and dynamic information under
such time-series visual stimuli remain to be further explored. In this work,
considering abundant recurrent connections in the mouse visual system, we
design a recurrent module based on the hierarchy of the mouse cortex and add it
into Deep Spiking Neural Networks, which have been demonstrated to be a more
compelling computational model for the visual cortex. Using Time-Series
Representational Similarity Analysis, we measure the representational
similarity between networks and mouse cortical regions under natural movie
stimuli. Subsequently, we conduct a comparison of the representational
similarity across recurrent/feedforward networks and image/video training
tasks. Trained on the video action recognition task, recurrent SNN achieves the
highest representational similarity and significantly outperforms feedforward
SNN trained on the same task by 15% and the recurrent SNN trained on the image
classification task by 8%. We investigate how static and dynamic
representations of SNNs influence the similarity, as a way to explain the
importance of these two forms of representations in biological neural coding.
Taken together, our work is the first to apply deep recurrent SNNs to model the
mouse visual cortex under movie stimuli and we establish that these networks
are competent to capture both static and dynamic representations and make
contributions to understanding the movie information processing mechanisms of
the visual cortex
A Validation Approach to Over-parameterized Matrix and Image Recovery
In this paper, we study the problem of recovering a low-rank matrix from a
number of noisy random linear measurements. We consider the setting where the
rank of the ground-truth matrix is unknown a prior and use an overspecified
factored representation of the matrix variable, where the global optimal
solutions overfit and do not correspond to the underlying ground-truth. We then
solve the associated nonconvex problem using gradient descent with small random
initialization. We show that as long as the measurement operators satisfy the
restricted isometry property (RIP) with its rank parameter scaling with the
rank of ground-truth matrix rather than scaling with the overspecified matrix
variable, gradient descent iterations are on a particular trajectory towards
the ground-truth matrix and achieve nearly information-theoretically optimal
recovery when stop appropriately. We then propose an efficient early stopping
strategy based on the common hold-out method and show that it detects nearly
optimal estimator provably. Moreover, experiments show that the proposed
validation approach can also be efficiently used for image restoration with
deep image prior which over-parameterizes an image with a deep network.Comment: 29 pages and 9 figure
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