4,016 research outputs found
Deep Learning-Based Machinery Fault Diagnostics
This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
On the transition to turbulence of wall-bounded flows in general, and plane Couette flow in particular
The main part of this contribution to the special issue of EJM-B/Fluids
dedicated to Patrick Huerre outlines the problem of the subcritical transition
to turbulence in wall-bounded flows in its historical perspective with emphasis
on plane Couette flow, the flow generated between counter-translating parallel
planes. Subcritical here means discontinuous and direct, with strong
hysteresis. This is due to the existence of nontrivial flow regimes between the
global stability threshold Re_g, the upper bound for unconditional return to
the base flow, and the linear instability threshold Re_c characterized by
unconditional departure from the base flow. The transitional range around Re_g
is first discussed from an empirical viewpoint ({\S}1). The recent
determination of Re_g for pipe flow by Avila et al. (2011) is recalled. Plane
Couette flow is next examined. In laboratory conditions, its transitional range
displays an oblique pattern made of alternately laminar and turbulent bands, up
to a third threshold Re_t beyond which turbulence is uniform. Our current
theoretical understanding of the problem is next reviewed ({\S}2): linear
theory and non-normal amplification of perturbations; nonlinear approaches and
dynamical systems, basin boundaries and chaotic transients in minimal flow
units; spatiotemporal chaos in extended systems and the use of concepts from
statistical physics, spatiotemporal intermittency and directed percolation,
large deviations and extreme values. Two appendices present some recent
personal results obtained in plane Couette flow about patterning from numerical
simulations and modeling attempts.Comment: 35 pages, 7 figures, to appear in Eur. J. Mech B/Fluid
Mean-Field Theory of Meta-Learning
We discuss here the mean-field theory for a cellular automata model of
meta-learning. The meta-learning is the process of combining outcomes of
individual learning procedures in order to determine the final decision with
higher accuracy than any single learning method. Our method is constructed from
an ensemble of interacting, learning agents, that acquire and process incoming
information using various types, or different versions of machine learning
algorithms. The abstract learning space, where all agents are located, is
constructed here using a fully connected model that couples all agents with
random strength values. The cellular automata network simulates the higher
level integration of information acquired from the independent learning trials.
The final classification of incoming input data is therefore defined as the
stationary state of the meta-learning system using simple majority rule, yet
the minority clusters that share opposite classification outcome can be
observed in the system. Therefore, the probability of selecting proper class
for a given input data, can be estimated even without the prior knowledge of
its affiliation. The fuzzy logic can be easily introduced into the system, even
if learning agents are build from simple binary classification machine learning
algorithms by calculating the percentage of agreeing agents.Comment: 23 page
A Clustering and SVM Regression Learning-Based Spatiotemporal Fuzzy Logic Controller with Interpretable Structure for Spatially Distributed Systems
Many industrial processes and physical systems are spatially distributed systems. Recently, a novel 3-D FLC was developed for such systems. The previous study on the 3-D FLC was concentrated on an expert knowledge-based approach. However, in most of situations, we may lack the expert knowledge, while input-output data sets hidden with effective control laws are usually available. Under such circumstance, a data-driven approach could be a very effective way to design the 3-D FLC. In this study, we aim at developing a new 3-D FLC design methodology based on clustering and support vector machine (SVM) regression. The design consists of three parts: initial rule generation, rule-base simplification, and parameter learning. Firstly, the initial rules are extracted by a nearest neighborhood clustering algorithm with Frobenius norm as a distance. Secondly, the initial rule-base is simplified by merging similar 3-D fuzzy sets and similar 3-D fuzzy rules based on similarity measure technique. Thirdly, the consequent parameters are learned by a linear SVM regression algorithm. Additionally, the universal approximation capability of the proposed 3-D fuzzy system is discussed. Finally, the control of a catalytic packed-bed reactor is taken as an application to demonstrate the effectiveness of the proposed 3-D FLC design
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
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