Hector Rodriguez Rangel

Instituto Tecnológico de Culiacán, Culiacán, Sinaloa · Maestria en Ciencias en Computacion

On a NVIDIA Jetson Nano device, this study illustrates a unique and original usage of automated control and artificial intelligence algorithms for angular velocity estimates of a first-order manipulator device. A platform can be used as a position estimation platform using computer vision and three state estimation algorithms: sliding mode differen...

One-stage production of carbohydrate-enriched microalgae biomass in wastewater is a promising option to obtain biofuels. Understanding the interaction of water quality parameters such as nutrients, carbon, internal carbohydrates, and microbial composition in the culture is crucial for efficient operation and viable large-scale cultivation. Bioproce...

Automobiles have increased urban mobility, but traffic accidents have also increased. Therefore, road safety is a significant concern involving academics and government. Transit studies are the main supply for studying road accidents, congestion, and flow traffic, allowing the understanding of traffic flow. They require special equipment (sensors)...

Cyanobacterial biomass has constituted a crucial third and fourth-generation biofuel material, with great potential to synthesize a wide range of metabolites, mainly carbohydrates. Lately, carbohydrate-based biofuels from cyanobacteria, such as bioethanol, biohydrogen, and biobutanol, have attracted attention as a sustainable alternative to petrole...

Deep learning has made essential contributions to classification and detection tasks applied to precision agriculture; however, it is vitally important to move towards an adoption of these techniques and algorithms through low-cost and low-consumption devices for daily use in crop fields. In this paper, we present the training and evaluation of fou...

In this research, we implement an intelligent quantitative model to assess a specific qualitative intelligence scale in children between 5 and 8 years old, based on augmented reality and the well known WISC-IV test. The output of the model is a cognitive factor associated with the analogical reasoning level of the child, and the ulterior analysis o...

The electrocardiogram records the heart’s electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classifi...

An intelligent tutoring system is used as an efficient self-learning tutor, where decisions are based on the affective state of the user. These detected emotions are what experts call basic emotions and the best-known recognition technique is the recognition of facial expressions. A convolutional neural network (CNN) can be used to identify emotion...

Forecasting is the process of making value predictions given historical samples of the observed variable. In some cases, there are missing points in the series because of problems in the collector device such as connectivity, maintenance, or meteorological factors. In addition, time series would exhibit a chaotic behavior, which makes the predictio...

This article presents a comparison of wind speed forecasting techniques, starting with the Auto-regressive Integrated Moving Average, followed by Artificial Intelligence-based techniques. The objective of this article is to compare these methods and provide readers with an idea of what method(s) to apply to solve their forecasting needs. The Artifi...

In recent years, the frequent appearance of obstacles on roads has been increasing. Opportune obstacle detection is crucial in driver-assistance systems to prevent traffic incidents. Artificial vision has been used to design advanced driver-assistance systems. Driver-assistance allows avoiding collisions or (mortal) accidents by offering technologi...

This work presents the application of a convolutional neural network (CNN) used to identify emotions through taken images to students, which are learning Java language with an Intelligent Learning Environment. The CNN contains three convolutional layers, three max-pooling layers, and three neural networks with intermediate dropout connections. The...

This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the patter...

Resumen En México concluir una carrera universitaria no es garantía de incorporase al campo laboral de manera inmediata, cada vez es más difícil conseguir empleo con un salario competitivo. El propósito principal de esta investigación es realizar un análisis, mediante la utilización de regresión lineal múltiple, que permita identificar las barreras...

Learning-oriented emotions have not been studied by emotion recognition systems. These emotions have not been taken into account by other studies despite their importance in educational context. This work presents a recognition system which uses deep learning approach using convolutional neural network for solving that problem. A convolutional arch...

Currently, electric energy is used in practically all modern human activities. Most of the energy produced came from fossil fuels, making irreversible damage to the environment. Lately, there has been an effort by nations to produce energy using clean methods, such as solar and wind energy, among others. Wind energy is one of the cleanest alternati...

En este artıculo se presenta un estudio realizado a la prediccion a corto plazo de la velocidad del viento en series de tiempo incompletas. Se ha propuesto realizar este estudio dado el incremento en el interes hacia la transicion global de la produccion de energıas limpias. Siendo el pronostico de dicha variable de suma importancia para las etapas...

In literature, University Course Timetabling Problem (UCTP) is a well known combinational problem. The main reasons to study this problem are the intrinsic importance at the interior of universities, the exponential number of solutions, and the distinct types of approaches to solve this problem. Due to the exponential number of solutions (combinati...

Efficient management of a drinking water network reduces the economic costs related to water production and transport (pumping). Model predictive control (MPC) is nowadays a quite well-accepted approach for the efficient management of the water networks because it allows formulating the control problem in terms of the optimization of the economic c...

The use of false or erroneous data can lead to wrong decisions when operating a system. In case of a water distribution network, the use of incorrect data could lead to errors in the billing system, waste of energy, incorrect management of control elements, etc. This paper is focused on detecting flow meters reading abnormalities by exploiting the...

Counting the number of words and lines that a user reads is important for many educational purposes - e.g., the reading speed is a key factor to improve learning, intelligent systems can suggest text that must be read to achieve a determined learning objective. The eye tracking technology is commonly used to analyze the user reading habits. Countin...

We explore the use of qualitative reasoning to predict the behaviour of a dynamical system given a change in one of its parameters based upon a qualitative representation of its bifurcation diagram. We present three algorithms to perform this task. The first algorithm generates a qualitative representation from a quantitative representation of the...

The evolutionary design of time series forecasters is a field that has been explored for several years now. In this paper, a complete design and training of ARMA (Auto-Regressive Moving Average) and ANN (Artificial Neural Networks) models through the use of Evolutionary Computation is presented. That is, given a time series, our proposal (EDFM – Ev...

Evolutionary design of time series predictors is a field that has been explored for several years now. The levels of design vary in the many works reported in the field. We decided to perform a complete design and training of ARIMA models using Evolutionary Computation. This decision leads to high dimensional search spaces, whose size increases exp...

The design of models for time series prediction has found a solid foundation on statistics. Recently, artificial neural networks have been a good choice as approximators to model and forecast time series. Designing a neural network that provides a good approximation is an optimization problem. Given the many parameters to choose from in the design...

In this paper we propose a qualitative representation for 3D algorithms that given a numeric discrete sampling of a function, produce a qualitative version of it, expressed in the proposed representation. The data may present noisy due to errors, numerical imprecision, etc. We start filtering the information, then we segment the information into mo...

The design of models for time series prediction has found a solid foundation on statistics. Recently, artificial neural networks have been a good choice as approximators to model and forecast time series. Designing a neural network that provides a good approximation is an optimization problem. Given the many parameters to choose from in the design...