73,140 research outputs found
Event Detection and Predictive Maintenance using Component Echo State Networks
With a growing number of sensors collecting information about systems in indus- try and infrastructure, one wants to extract useful information from this data. The goal of this project is to investigate the applicability of Echo State Net- work techniques to time-varying classification of multivariate time series from primarily mechanical and electrical systems. Two relevant technical problems are predicting impending failure of systems (predictive maintenance), and clas- sifying a common event related to the system (event detection). In this project, they are formulated as a supervised machine learning problem on a multivariate time series. For this problem, Echo State Networks (ESN) have proven effective. However, applying these algorithms to new data sets involves a lot of guesswork as to how the algorithm should be configured to model the data effectively. In this work, a modification of the Echo State Network (ESN) model is presented, that helps to remove some of this guesswork. The new algorithm uses specifically structured components in order to facilitate the generation of relevant features by the ESN. The algorithm is tested on two easy event detection data sets, and one hard predictive maintenance data set. The results are compared to Support Vector Machine and Multilayer Perceptron classifiers, as well as to a basic ESN, which is also implemented as a reference. The component ESN successfully generates promising features, and outperforms the minimum complexity ESN as well as the standard classifiers
Fast and compact self-stabilizing verification, computation, and fault detection of an MST
This paper demonstrates the usefulness of distributed local verification of
proofs, as a tool for the design of self-stabilizing algorithms.In particular,
it introduces a somewhat generalized notion of distributed local proofs, and
utilizes it for improving the time complexity significantly, while maintaining
space optimality. As a result, we show that optimizing the memory size carries
at most a small cost in terms of time, in the context of Minimum Spanning Tree
(MST). That is, we present algorithms that are both time and space efficient
for both constructing an MST and for verifying it.This involves several parts
that may be considered contributions in themselves.First, we generalize the
notion of local proofs, trading off the time complexity for memory efficiency.
This adds a dimension to the study of distributed local proofs, which has been
gaining attention recently. Specifically, we design a (self-stabilizing) proof
labeling scheme which is memory optimal (i.e., bits per node), and
whose time complexity is in synchronous networks, or time in asynchronous ones, where is the maximum degree of
nodes. This answers an open problem posed by Awerbuch and Varghese (FOCS 1991).
We also show that time is necessary, even in synchronous
networks. Another property is that if faults occurred, then, within the
requireddetection time above, they are detected by some node in the locality of each of the faults.Second, we show how to enhance a known
transformer that makes input/output algorithms self-stabilizing. It now takes
as input an efficient construction algorithm and an efficient self-stabilizing
proof labeling scheme, and produces an efficient self-stabilizing algorithm.
When used for MST, the transformer produces a memory optimal self-stabilizing
algorithm, whose time complexity, namely, , is significantly better even
than that of previous algorithms. (The time complexity of previous MST
algorithms that used memory bits per node was , and
the time for optimal space algorithms was .) Inherited from our proof
labelling scheme, our self-stabilising MST construction algorithm also has the
following two properties: (1) if faults occur after the construction ended,
then they are detected by some nodes within time in synchronous
networks, or within time in asynchronous ones, and (2) if
faults occurred, then, within the required detection time above, they are
detected within the locality of each of the faults. We also show
how to improve the above two properties, at the expense of some increase in the
memory
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to
predict real valued time-series. The method consists in projecting the output
of the internal layer of the network on a space with lower dimensionality,
before training the output layer to learn the target task. Notably, we enforce
a regularization constraint that leads to better generalization capabilities.
We evaluate the performances of our approach on several benchmark tests, using
different techniques to train the readout of the network, achieving superior
predictive performance when using the proposed framework. Finally, we provide
an insight on the effectiveness of the implemented mechanics through a
visualization of the trajectory in the phase space and relying on the
methodologies of nonlinear time-series analysis. By applying our method on well
known chaotic systems, we provide evidence that the lower dimensional embedding
retains the dynamical properties of the underlying system better than the
full-dimensional internal states of the network
Efficient Synthesis of Room Acoustics via Scattering Delay Networks
An acoustic reverberator consisting of a network of delay lines connected via
scattering junctions is proposed. All parameters of the reverberator are
derived from physical properties of the enclosure it simulates. It allows for
simulation of unequal and frequency-dependent wall absorption, as well as
directional sources and microphones. The reverberator renders the first-order
reflections exactly, while making progressively coarser approximations of
higher-order reflections. The rate of energy decay is close to that obtained
with the image method (IM) and consistent with the predictions of Sabine and
Eyring equations. The time evolution of the normalized echo density, which was
previously shown to be correlated with the perceived texture of reverberation,
is also close to that of IM. However, its computational complexity is one to
two orders of magnitude lower, comparable to the computational complexity of a
feedback delay network (FDN), and its memory requirements are negligible
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