Diego Benitez

Universidad San Francisco de Quito (USFQ) · College of Science and Engineering

A strategy for optimizing the settings of a dynamical sliding mode controller using an artificial bee colony optimization algorithm is proposed in this paper. The performance of the obtained controller is then evaluated and compared to that of a conventional PID and a dynamical sliding mode controller that has been optimized through a heuristics-ba...

In the past few years, bio-inspired optimization algorithms have shown to be an excellent way to solve a wide range of complex computing problems in science and engineering. This paper compares bio-inspired algorithms to better understand and measure how well they find the best tuning parameters for a Dynamic Sliding Mode Control for integrating sy...

This work presents a hybrid sliding mode controller approach for level control in the nuclear power plant steam generators. In a nuclear power plant, the steam generator is one of the essential pieces of equipment. Therefore, this paper aims at a robust hybrid scheme that merges internal model control concepts, sliding mode control methodology, and...

The COVID-19 pandemic has created a worldwide healthcare crisis. Convolutional Neural Networks (CNNs) have recently been used with encouraging results to help detect COVID-19 from chest X-ray images. However, to generalize well to unseen data, CNNs require large labeled datasets. Due to the lack of publicly available COVID-19 datasets, most CNNs ap...

Captive environments trigger the propagation and multiplication of parasites among different reptile species, thus weakening their immune response and causing infections and diseases. Technological advances of convolutional neural networks have opened a new field for detecting and classifying diseases which have shown great potential to overcome th...

This work compares five different distance metrics (i.e., Euclidean, Chebyshev, Manhattan, Mahalanobis, and Canberra) implemented in the weighted Hausdorff distance (WHD) as part of the loss function during the training and validation of a fully convolutional neural network (FCNN) model for detecting ladybird beetle specimens. The FCNN-based detect...

This work compares face gesture recognition methods based on deep learning convolutional neural network and autoencoder architectures named DCNN1, DCNN2, DCNN3, DCNN4, and DCNN+Autoencoder, that maximize the classification performance on single and mixing databases. We validated the proposed architectures on four different databases: Jaffe, CK+, FA...

Captive environments trigger the propagation and multiplication of parasites among different reptile species, thus weakening their immune response and causing infections and diseases. Technological advances of convolutional neural networks have opened a new field for detecting and classifying diseases which have shown great potential to overcome th...

With the growing ability to collect large volumes of volcano seismic data, the detection and labeling process of these records is increasingly challenging. Clearly, analyzing all available data through manual inspection is no longer a viable option. Supervised Machine Learning models might be considered to automatize the analysis of data acquired b...

Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first...

In this work, we propose a ladybird beetle detector based on a deep learning classifier and the weighted Hausdorff distance as a loss function. The detector was trained and validated using a ten-fold cross-validation method on a database composed of 2,633 wildlife images with ladybird beetles. Despite the detector performance was assessed using fou...

Novel artificial intelligence prediction algorithms use deep learning techniques, i.e., recurrent neural networks and convolutional neural networks, to predict financial time series. Also, autoencoders have gained notoriety to extract features from latent space data and decode them for predictions. This paper compares several deep learning architec...

This work compares face gesture recognition methods based on deep learning convolutional neural network (DCNN) architectures named DCNN1, DCNN2, DCNN3, DCNN4, and DCNN+Autoencoder, that maximize the classification performance on single and mixing datasets. We validated the proposed architectures on three different databases: Jaffe, CK+, and the com...

In this paper, we propose a new automated method based on deep convolutional neural networks to detect and track critically endangered hammerhead sharks in video sequences. The proposed method improved the standard YOLOv3 deep architecture by adding 18 more layers (16 convolutional and 2 Yolo layers), which increased the model performance in detect...

In this work, we proposed a new method to classify long-period and volcano-tectonic spectrogram images using eight different deep learning architectures. The developed method used three deep convolutional neural networks named DCNN1, DCNN2, and DCNN3, three deep convolutional neural networks combined with deep recurrent neural networks named DCNN-R...

Culicoides biting midges are transmission vectors of various diseases affecting humans and animals around the world. An optimal and fast classification method for these and other species have been a challenge and a necessity, especially in areas with limited resources and public health problems. In this work, we developed a mobile application to cl...

Social media is an important information outlet and a new political landscape for politicians. In fact, politicians use social media to promote their candidacies while running for office. In this paper, we discuss about an application prototype built to measure the closeness of a candidate electoral manifesto to hers/his online campaign. We use fou...

We explored four different clustering-based classifiers to categorize two different volcanic seismic events and to find possible overlapping signals that could occur at the same time or immediately after seismic events occurrence. The BFR classifier with \(k=2\) was chosen as the best out of 36 explored models statistically (\(p<0.05\)), reaching a...

This work proposes a new approach based on a suit combination of mathematical morphology and similarity criteria techniques to classify long-period and volcano-tectonic seismic events of the Cotopaxi volcano. The proposed method explores the seismic signal domain to compute a new feature space based on the edges map of the seismic events pattern re...

Adequate detection and classification of seismic events are crucial for understanding the internal status of a Volcano. Machine learning-based classifiers use different features from the time, frequency, and scale domains related to seismic events. Regarding power spectrum-based features, several methods can be used to compute such features. Howeve...

Fast and accurate identification of biting midges is crucial in the study of Culicoides-borne diseases. In this work, we propose a two-stage method for automatically analyzing Culicoides (Diptera: Ceratopogonidae) species. First, an image preprocessing task composed of median and Wiener filters followed by equalization and morphological operations...

We systematically and comprehensively tested almost 100 feature groups in four commonly used automatic event classifiers to find the combinations that maximize the classification performance for long-period and volcano-tectonic seismic events at Cotopaxi volcano, Ecuador. The feature groups tested fall into the following categories: time, fast-Four...

This paper explores the use of six different clustering-based methods to classify long-period and volcano-tectonic seismic events and to find possible overlapping signals of non-volcanic origin that could occur at the same time or immediately after the occurrence of volcano-seismic events. According to the explored classifiers space, the BIRCH meth...

This article proposes a new volcano seismic signal descriptor for improving the area under the receiver operating characteristic curve (AUC) in the classification of long-period (LP) and volcano-tectonic (VT) seismic events. It aims to describe a volcanic seismic event from a different and novel point of view that involves image processing techniqu...

In this work, we present the development, description, and performance evaluation of two volcano seismological datasets: one containing raw seismic signals (MicSigV1) and another containing a set of 84 distinctive attributes or features for bench-marking (SeisBenchV1), which was extracted from the MicSigV1 dataset, recorded at Cotopaxi volcano in E...

Nowadays, the contaminated and poor air quality that is inhaled by the city-population in industrialized cities around the world, has led to one of the main causes of premature death due to respiratory diseases. Therefore, the improvement of air quality becomes a priority. In this sense, the current study aimed to design and implement a visual anal...

Air pollution is a problem that causes adverse effects, which tends to interfere with human comfort, health or well-being, and that may cause serious environmental damage. In this regard, this study aims to analyze large data sets generated by real-time wireless sensor networks that determine different air pollutants. Business Intelligence and Data...

This paper proposes an exhaustive evaluation of five different filter-based feature selection methods in combination with a Gaussian mixture model classifier for the classification of long-period (LP) and volcano-tectonic (VT) seismic events recorded at Cotopaxi volcano in Ecuador. The experimentation included both exploring and ranking search spac...

Air pollution is a problem that causes adverse effects, which tends to interfere with human comfort, health or well-being, and that may cause serious environmental damage. In this regard, this study aims to analyze large data sets generated by real-time wireless sensor networks that determine different air pollutants. Business Intelligence and Data...

Air pollution continues to grow at an alarming rate, decreasing the quality of life around the world. As part of preventive measures, this paper presents the design and implementation of a secure and low-cost real-time air pollution monitoring system. In such sense, a three-layer architecture system was implemented. The first layer contains sensors...

This paper describes the design and validation of a test bench system for the diagnostics and condition monitoring of three-phase induction motors using current power spectrum analysis techniques. Faults produced harmonics in the stator current are measured using Hall Effect sensors and analog-digital converters in each of the phases. The equipment...

This paper presents a study in order to identify the value range of the main parameters within Carrier Sense Multiple Access (CSMA) defined in IEEE 802.15.4 that guarantees a satisfactory Wireless Sensor Networks (WSN) performance for a volcano monitoring application. Moreover, this study performs the comparison among several test-beds in outdoor s...

A relative simple approach based on the computation of the area of a parametric curve produced by the 2D space representation of a set of parametric experimental functions defined by the signals of only two active EEG electrodes of a low cost neuroheadset (Emotiv EPOC) is proposed on this paper for the fast recognition of eye winks activity as cont...