Publicaciones

RL2, Publisher: Theory and Practice of Logic Programming, Link>

AUTHORS

Leopoldo Bertossi

ABSTRACT

We propose answer-set programs that specify and compute counterfactual interventions on entities that are input on a classification model. In relation to the outcome of the model, the resulting counterfactual entities serve as a basis for the definition and computation of causality-based explanation scores for the feature values in the entity under classification, namely responsibility scores. The approach and the programs can be applied with black-box models, and also with models that can be specified as logic programs, such as rule-based classifiers. The main focus of this study is on the specification and computation of best counterfactual entities, that is, those that lead to maximum responsibility scores. From them one can read off the explanations as maximum responsibility feature values in the original entity. We also extend the programs to bring into the picture semantic or domain knowledge. We show how the approach could be extended by means of probabilistic methods, and how the underlying probability distributions could be modified through the use of constraints. Several examples of programs written in the syntax of the DLV ASP-solver, and run with it, are shown.


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RL4, Publisher: SIAM J. Control Optim., Link>

AUTHORS

Eduardo Cerpa, Christophe Prieur, Constantinos Kitsos, Gildas Besancon

ABSTRACT

This paper is about the stabilization of a cascade system of $n$ linear Korteweg--de Vries equations in a bounded interval. It considers an output feedback control placed at the left endpoint of the last equation, while the output involves only the solution to the first equation. The boundary control problems investigated include two cases: a classical control on the Dirichlet boundary condition and a less standard one on its second-order derivative. The feedback control law utilizes the estimated solutions of a high-gain observer system, and the output feedback control leads to stabilization for any $n$ for the first boundary conditions case and for $n=2$ for the second one.


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RL4, Publisher: arXiv, Link>

AUTHORS

Roberto de A. Capistrano Filho, Eduardo Cerpa, Fernando A. Gallego

ABSTRACT

This paper studies the exponential stabilization of a Boussinesq system describing the two-way propagation of small amplitude gravity waves on the surface of an ideal fluid, the so-called Boussinesq system of the Korteweg–de Vries type. We use a Gramian-based method introduced by Urquiza to design our feedback control. By means of spectral analysis and Fourier expansion, we show that the solutions of the linearized system decay uniformly to zero when the feedback control is applied. The decay rate can be chosen as large as we want. The main novelty of our work is that we can exponentially stabilize this system of two coupled equations using only one scalar input.


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RL4, Publisher: Mathematical Control and Related Fields, Link>

AUTHORS

Eduardo Cerpa, Esteban Hernández, Christophe Prieur

ABSTRACT

In this paper the Single Particle Model is used to describe the behavior of a Li-ion battery. The main goal is to design a feedback input current in order to regulate the State of Charge (SOC) to a prescribed reference trajectory. In order to do that, we use the boundary ion concentration as output. First, we measure it directly and then we assume the existence of an appropriate estimator, which has been established in the literature using voltage measurements. By applying backstepping and Lyapunov tools, we are able to build observers and to design output feedback controllers giving a positive answer to the SOC tracking problem. We provide convergence proofs and perform some numerical simulations to illustrate our theoretical results.


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RL1, Publisher:, Link>

AUTHORS

Domingo Mery, Christian Pieringer

ABSTRACT

In this chapter, we will cover known classifiers that can be used in X-ray testing. Several examples will be presented using Python. The reader can easily modify the proposed implementations in order to test different classification strategies. We will then present how to estimate the accuracy of a classifier using hold-out, cross-validation and leave-one-out. Finally, we will present an example that involves all steps of a pattern recognition problem, i.e., feature extraction, feature selection, classifier’s design, and evaluation. We will thus propose a general framework to design a computer vision system in order to select—automatically—from a large set of features and a bank of classifiers, those features and classifiers that can achieve the highest performance.

 

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RL1, Publisher:, Link>

AUTHORS

Domingo Mery, Christian Pieringer

ABSTRACT

In this chapter, we present the dataset that is used in this book to illustrate and test several methods. The database consists of 23,189 X-ray images. The images are organized in a public database called GDXray+that can be used free of charge, but for research and educational purposes only. The database includes five groups of X-ray images: castings, welds, baggage, natural objects, and settings. Each group has several series, and each series several X-ray images. Most of the series are annotated or labeled. In such cases, the coordinates of the bounding boxes of the objects of interest or the labels of the images are available in standard text files. The size of GDXray+is 4.5 GB and it can be downloaded from our website.


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RL1, Publisher:, Link>

AUTHORS

Domingo Mery, Christian Pieringer

ABSTRACT

In order to evaluate the performance of computer vision techniques, computer simulation can be a useful tool. In this chapter, we review some basic concepts of the simulation of X-ray images, and present simple geometric and imaging models that can be used in the simulation. We explain the basic simulation principles and we address some techniques of simulated defects (that can be used to assess the performance of a computer vision method for automated defect recognition) and simulation of threat objects (that can be used to assess the performance of computer vision methods, to enhance the training dataset, or to improve a training program for human inspectors). Afterwards, the chapter gives an overview of the use of Generative Adversarial Networks (GANs) in the simulation of realistic X-ray images. Finally, we present ‘aRTist’, a simulation software that can be used to generate very realistic X-ray images. The chapter also has some Python examples that the reader can run and follow easily.


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RL1, Publisher:, Link>

AUTHORS

Domingo Mery, Christian Pieringer

ABSTRACT

In this chapter, we cover several topics that are used to represent an X-ray image (or a specific region of an X-ray image). This representation means that new features are extracted from the original image that can give us more information than the raw information expressed as a matrix of gray values. This kind of information is extracted as features or descriptors, i.e., a set of values, that can be used in pattern recognition problems such as object recognition, defect detection, etc. The chapter explains geometric and intensity features, and local descriptors and sparse representations that are very common in computer vision applications. It is worthwhile to mention, that the features mentioned in this chapter are called handcrafted features, in contrast to the learned features that are explained in Chap. 7 using deep learning techniques. Finally, the chapter addresses some feature selection techniques that can be used to chose which features are relevant in terms of extraction.


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RL1, Publisher:, Link>

AUTHORS

Domingo Mery, Christian Pieringer

ABSTRACT

X-ray testing has been developed for the inspection of materials or objects, where the aim is to analyze—nondestructively—those inner parts that are undetectable to the naked eye. Thus, X-ray testing is used to determine if a test object deviates from a given set of specifications. Typical applications are the inspection of automotive parts, quality control of welds, baggage screening, analysis of food products, inspection of cargos, and quality control of electronic circuits. In order to achieve efficient and effective X-ray testing, automated and semi-automated systems based on computer vision algorithms are being developed to execute this task. In this book, we present a general overview of computer vision approaches that have been used in X-ray testing in the last decades. In this chapter, we offer an introduction to our book by covering relevant issues of X-ray testing.


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RL1, Publisher:, Link>

AUTHORS

Domingo Mery

ABSTRACT

Deep learning has been inspired by ideas from neuroscience. The key idea of deep learning is to replace handcrafted features (explained in details in Chap. 5) with features that are learned efficiently using a hierarchical feature extraction approach. Usually, the learned features are so discriminative that no sophisticated classifiers are required. In last years, deep learning has been successfully used in image and video recognition, and it has been established as the state of the art in many areas such as computer vision, machine translation, and natural language processing. In comparison with other computer vision applications, we have seen that the introduction of techniques based on deep learning in computer vision for X-ray testing has been rather slow. However, there are many methods based on deep learning that have been designed and tested in some X-ray testing applications. In this chapter, we review many relevant concepts of deep learning that can be used in computer vision for X-ray testing. We covered the theory and practice of deep learning techniques in real X-ray testing problems. The chapter explained neural networks, Convolutional Neural Network (CNN) that can be used in classification problems, pre-trained models, transfer learning that are used in sophisticated models, Generative Adversarial Networks (GANs) to generate synthetic images, and modern detection methods that are used to classify and localize objects in an image. In addition, for every method, we give not only the basic concepts but also practical details in real X-ray testing examples that have been implemented in Python.


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