688 research outputs found
Integration of different models in the design of chemical processes: Application to the design of a power plant
With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.Spanish Ministry of Science and Innovation (CTQ2012-37039-C02-02)
Robust computational intelligence techniques for visual information processing
The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training.
Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined.
Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed.
The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola
Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies
Gradient-based methods have been widely used for system design and
optimization in diverse application domains. Recently, there has been a renewed
interest in studying theoretical properties of these methods in the context of
control and reinforcement learning. This article surveys some of the recent
developments on policy optimization, a gradient-based iterative approach for
feedback control synthesis, popularized by successes of reinforcement learning.
We take an interdisciplinary perspective in our exposition that connects
control theory, reinforcement learning, and large-scale optimization. We review
a number of recently-developed theoretical results on the optimization
landscape, global convergence, and sample complexity of gradient-based methods
for various continuous control problems such as the linear quadratic regulator
(LQR), control, risk-sensitive control, linear quadratic
Gaussian (LQG) control, and output feedback synthesis. In conjunction with
these optimization results, we also discuss how direct policy optimization
handles stability and robustness concerns in learning-based control, two main
desiderata in control engineering. We conclude the survey by pointing out
several challenges and opportunities at the intersection of learning and
control.Comment: To Appear in Annual Review of Control, Robotics, and Autonomous
System
Global Optimization of Gaussian processes
Gaussian processes~(Kriging) are interpolating data-driven models that are
frequently applied in various disciplines. Often, Gaussian processes are
trained on datasets and are subsequently embedded as surrogate models in
optimization problems. These optimization problems are nonconvex and global
optimization is desired. However, previous literature observed computational
burdens limiting deterministic global optimization to Gaussian processes
trained on few data points. We propose a reduced-space formulation for
deterministic global optimization with trained Gaussian processes embedded. For
optimization, the branch-and-bound solver branches only on the degrees of
freedom and McCormick relaxations are propagated through explicit Gaussian
process models. The approach also leads to significantly smaller and
computationally cheaper subproblems for lower and upper bounding. To further
accelerate convergence, we derive envelopes of common covariance functions for
GPs and tight relaxations of acquisition functions used in Bayesian
optimization including expected improvement, probability of improvement, and
lower confidence bound. In total, we reduce computational time by orders of
magnitude compared to state-of-the-art methods, thus overcoming previous
computational burdens. We demonstrate the performance and scaling of the
proposed method and apply it to Bayesian optimization with global optimization
of the acquisition function and chance-constrained programming. The Gaussian
process models, acquisition functions, and training scripts are available
open-source within the "MeLOn - Machine Learning Models for Optimization"
toolbox~(https://git.rwth-aachen.de/avt.svt/public/MeLOn)
Gravitational Search and Harmony Search Algorithms for Solving the Chemical Kinetics Optimization Problems
The article is dedicated to the analysis of the global optimization algorithms application to the solution of inverse problems of chemical kinetics. Two heuristic algorithms are considered - the gravitational search algorithm and the harmony algorithm. The article describes the algorithms, as well as the application of these algorithms to the optimization of test functions. After that, these algorithms are used to search for the kinetic parameters of two chemical processes – propane pre-reforming on Ni-catalyst and catalytic isomerization of pentane-hexane fraction. For the first process both algorithms showed approximately the same solution, while for the second problem the gravitational search algorithm showed a smaller value of the minimizing function. Wherefore, it is concluded that on large-scale problems it is better to use the gravitational search algorithm rather than the harmony algorithm, while obtaining a smaller value of the minimizing function in a minimum time. On low-scale problems both algorithms showed approximately the same result, while demonstrating the coincidence of the calculated data with the experimental ones
Lost in optimisation of water distribution systems? A literature review of system operation
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Optimisation of the operation of water distribution systems has been an active research field for almost half a
century. It has focused mainly on optimal pump operation to minimise pumping costs and optimal water
quality management to ensure that standards at customer nodes are met. This paper provides a systematic
review by bringing together over two hundred publications from the past three decades, which are relevant to
operational optimisation of water distribution systems, particularly optimal pump operation, valve control
and system operation for water quality purposes of both urban drinking and regional multiquality water
distribution systems. Uniquely, it also contains substantial and thorough information for over one hundred
publications in a tabular form, which lists optimisation models inclusive of objectives, constraints, decision
variables, solution methodologies used and other details. Research challenges in terms of simulation models,
optimisation model formulation, selection of optimisation method and postprocessing needs have also been
identified
Dagstuhl News January - December 2006
"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic
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