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Jesús Figueroa-Nazuno

Bio: Jesús Figueroa-Nazuno is an academic researcher from Instituto Politécnico Nacional. The author has contributed to research in topics: Statistical classification & Feature extraction. The author has an hindex of 4, co-authored 7 publications receiving 26 citations.

Papers
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Journal ArticleDOI
TL;DR: The objective of this work is to establish a new methodology for practical application of nonlinear dynamics in seismic pattern/attributes recognition, an evolving and challenging engineering field.
Abstract: The current analysis of earthquakes is typically based on linear mathematical models that may fail to describe and forecast particular behaviors, because in many cases the data complexity may induce a highly non linear behavior. In this paper the implementation of an alternative method for seismic time series analysis is presented. The RPs (Recurrence Plots) enables recognition and treatment of measured accelerations. An RP obtained from seismic data allows a more efficient interpretation of the ground motions and this explanation contributes to characterize materials and responses. The nonlinear attributes from RPs analysis can be used as filters to reveal patterns or be combined to predict a seismic property. Automated seismic data characterization, based on nonlinear seismic attributes, could rewrite the rules of earthquake phenomena interpretation. The objective of this work is to establish a new methodology for practical application of nonlinear dynamics in seismic pattern/attributes recognition, an evolving and challenging engineering field.

10 citations

Book
28 Sep 2016
TL;DR: This book provides arguments supporting that creativity, as storytelling, can be emulated through computer programs, and proposes a heuristic that uses simple syntactic and semantic properties found in a text corpus in order to generate novel and coherent fiction texts based on what has been already written.
Abstract: Are mind and machine capable of solving the same tasks? Creativity is one of the arguments that some philosophers and psychologists use as a proof of what computers cannot achieve; however, these arguments might be based on a misconception of what both intelligence and creativity mean. This book provides arguments supporting that creativity, as storytelling, can be emulated through computer programs. The assumption of creativity presents a major problem: Complexity. Even if we consider creativity just as a product of novel ways of achieving a goal, the number of combinations found when dealing with the ‘real world’ is astronomically huge. We can recall The Library of Babel (Borges, 1944), a library that contains any possible book that could be written in the history of humanity. This metaphor reveals the combinatory problem that emerges if a brute force algorithm is designed to generate texts. According to our hypothesis, our proposal is a heuristic that uses simple syntactic and semantic properties found in a text corpus in order to generate novel and coherent fiction texts based on what has been already written.

9 citations

Journal ArticleDOI
TL;DR: This work focuses on feature extraction algorithms and highlights Common Spatial Pattern (CSP) as a method of feature extraction, which benefited from using CSP instead of FDTW, LPC, PCA or ICA for feature extraction.

7 citations

Journal ArticleDOI
TL;DR: A neurofuzzy system is proposed to map the values obtained from cone penetration tests into the dynamic properties of Mexico City clays, and it is possible to achieve profies of shear modulus and damping ratios versus shear strain curves, from which soil deposits can be characterized dynamically at a reduced cost.
Abstract: Proper characterization of the dynamic behaviour of soil deposits is of utmost importance in earthquake ground-response analyses. Cone-tip penetration resistances, which are usually obtained in a typical geotechnical study for foundation design, can be used to evaluate the dynamic properties and thus to outline dynamically a given soil deposit. Because of the capacity of neurofuzzy techniques to combine the representational aspect of fuzzy models and the learning mechanisms of neural networks, in this paper a neurofuzzy system is proposed to map the values obtained from cone penetration tests into the dynamic properties of Mexico City clays. Utilizing this methodology, it is possible to achieve profies of shear modulus and damping ratios versus shear strain curves, from which soil deposits can be characterized dynamically at a reduced cost.

6 citations

Proceedings ArticleDOI
01 Oct 2016
TL;DR: In this work the fast algorithm Dynamic Time Warp (FDTW) is used as a method of feature extraction for 18 sets of EEG records to study the semantic relationship between pairs of nouns of concrete objects and how this relationship activity is reflected in EEG signals.
Abstract: In this work the fast algorithm Dynamic Time Warp (FDTW) is used as a method of feature extraction for 18 sets of EEG records. Each set contains 150 events of stimulation designed to study the semantic relationship between pairs of nouns of concrete objects such as "HORSE - SHEEP" and "SWING - MELON" and how this relationship activity is reflected in EEG signals. Based on these latter, different classifiers were trained in order to associate a set of signals to a previously learned human answer, pertaining to two classes: semantically related, or not semantically related. The results of classification accuracy were evaluated comparing with other 3 methods of feature extraction, and using 5 different classification algorithms. In all cases, classification accuracy was benefited from using FDTW instead of LPC, PCA or ICA for feature extraction.

1 citations


Cited by
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01 Mar 1995
TL;DR: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series and results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages.
Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

1,545 citations

Dissertation
01 Jan 2004

602 citations

Journal ArticleDOI
TL;DR: An overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.
Abstract: Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classicalmathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.

120 citations

Book ChapterDOI
01 Jan 2012
TL;DR: In this article, a brief overview of three selected AI techniques and their applications in geotechnical engineering, discusses some AI modeling aspects that need further attention, and provides insights into future directions and research challenges.
Abstract: Over the last decade or so, artificial intelligence (AI) has proved to provide a high level of competency in solving many geotechnical engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. This chapter presents a brief overview of three selected AI techniques and their applications in geotechnical engineering, discusses some AI modeling aspects that need further attention, and provides insights into future directions and research challenges.

47 citations

Journal ArticleDOI
TL;DR: The detection of an epileptic seizure based on sparse features using Fisher linear discriminant analysis classifiers is more suitable for a reliable, automatic epilept seizure detection system to enhance the patient’s care and the quality of life.
Abstract: In order to realize the automatic epileptic seizure detection, feature extraction and classification of electroencephalogram (EEG) signals are performed on the interictal, the pre-ictal, and the ictal status of epilepsy patients. There is no effective strategy for selecting the number of channels and spatial filters in feature extraction of multichannel EEG data. Therefore, this paper combined sparse idea and greedy search algorithm to improve the feature extraction of common space pattern. The feature extraction can effectively overcome the repeating selection problem of feature pattern in the eigenvector space by the traditional method. Then we used the Fisher linear discriminant analysis to realize the classification. The results show that the proposed method can get high classification accuracy using fewer data. For 10 subjects, the averaged accuracy of epilepsy detection is more than 99%. So, the detection of an epileptic seizure based on sparse features using Fisher linear discriminant analysis classifiers is more suitable for a reliable, automatic epileptic seizure detection system to enhance the patient’s care and the quality of life.

27 citations