1,796,447 research outputs found
Grammar-based Representation and Identification of Dynamical Systems
In this paper we propose a novel approach to identify dynamical systems. The
method estimates the model structure and the parameters of the model
simultaneously, automating the critical decisions involved in identification
such as model structure and complexity selection. In order to solve the
combined model structure and model parameter estimation problem, a new
representation of dynamical systems is proposed. The proposed representation is
based on Tree Adjoining Grammar, a formalism that was developed from linguistic
considerations. Using the proposed representation, the identification problem
can be interpreted as a multi-objective optimization problem and we propose a
Evolutionary Algorithm-based approach to solve the problem. A benchmark example
is used to demonstrate the proposed approach. The results were found to be
comparable to that obtained by state-of-the-art non-linear system
identification methods, without making use of knowledge of the system
description.Comment: Submitted to European Control Conference (ECC) 201
Sensor placement for fault location identification in water networks: A minimum test cover approach
This paper focuses on the optimal sensor placement problem for the
identification of pipe failure locations in large-scale urban water systems.
The problem involves selecting the minimum number of sensors such that every
pipe failure can be uniquely localized. This problem can be viewed as a minimum
test cover (MTC) problem, which is NP-hard. We consider two approaches to
obtain approximate solutions to this problem. In the first approach, we
transform the MTC problem to a minimum set cover (MSC) problem and use the
greedy algorithm that exploits the submodularity property of the MSC problem to
compute the solution to the MTC problem. In the second approach, we develop a
new \textit{augmented greedy} algorithm for solving the MTC problem. This
approach does not require the transformation of the MTC to MSC. Our augmented
greedy algorithm provides in a significant computational improvement while
guaranteeing the same approximation ratio as the first approach. We propose
several metrics to evaluate the performance of the sensor placement designs.
Finally, we present detailed computational experiments for a number of real
water distribution networks
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks
Person re-identification is an open and challenging problem in computer
vision. Existing approaches have concentrated on either designing the best
feature representation or learning optimal matching metrics in a static setting
where the number of cameras are fixed in a network. Most approaches have
neglected the dynamic and open world nature of the re-identification problem,
where a new camera may be temporarily inserted into an existing system to get
additional information. To address such a novel and very practical problem, we
propose an unsupervised adaptation scheme for re-identification models in a
dynamic camera network. First, we formulate a domain perceptive
re-identification method based on geodesic flow kernel that can effectively
find the best source camera (already installed) to adapt with a newly
introduced target camera, without requiring a very expensive training phase.
Second, we introduce a transitive inference algorithm for re-identification
that can exploit the information from best source camera to improve the
accuracy across other camera pairs in a network of multiple cameras. Extensive
experiments on four benchmark datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art unsupervised learning based
alternatives whilst being extremely efficient to compute.Comment: CVPR 2017 Spotligh
On a continuation approach in Tikhonov regularization and its application in piecewise-constant parameter identification
We present a new approach to convexification of the Tikhonov regularization
using a continuation method strategy. We embed the original minimization
problem into a one-parameter family of minimization problems. Both the penalty
term and the minimizer of the Tikhonov functional become dependent on a
continuation parameter.
In this way we can independently treat two main roles of the regularization
term, which are stabilization of the ill-posed problem and introduction of the
a priori knowledge. For zero continuation parameter we solve a relaxed
regularization problem, which stabilizes the ill-posed problem in a weaker
sense. The problem is recast to the original minimization by the continuation
method and so the a priori knowledge is enforced.
We apply this approach in the context of topology-to-shape geometry
identification, where it allows to avoid the convergence of gradient-based
methods to a local minima. We present illustrative results for magnetic
induction tomography which is an example of PDE constrained inverse problem
Learning to rank in person re-identification with metric ensembles
We propose an effective structured learning based approach to the problem of
person re-identification which outperforms the current state-of-the-art on most
benchmark data sets evaluated. Our framework is built on the basis of multiple
low-level hand-crafted and high-level visual features. We then formulate two
optimization algorithms, which directly optimize evaluation measures commonly
used in person re-identification, also known as the Cumulative Matching
Characteristic (CMC) curve. Our new approach is practical to many real-world
surveillance applications as the re-identification performance can be
concentrated in the range of most practical importance. The combination of
these factors leads to a person re-identification system which outperforms most
existing algorithms. More importantly, we advance state-of-the-art results on
person re-identification by improving the rank- recognition rates from
to on the iLIDS benchmark, to on the PRID2011
benchmark, to on the VIPeR benchmark, to on the
CUHK01 benchmark and to on the CUHK03 benchmark.Comment: 10 page
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