Espinosa Loera, Ricardo Abel
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Espinosa Loera, Ricardo Abel
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Espinosa Loera, Ricardo Abel
Alternative Name
respinosa
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57211517018

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Now showing 1 - 10 of 18
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Design and Implementation of a Node Geolocation System for Fire Monitoring through LoRaWAN

2020 , Pedro Luna , Gutiérrez, Sebastián , Espinosa Loera, Ricardo Abel

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A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-Based Photometric Image Enhancement Models

2022 , Axel García-Vega , Ricardo Espinosa , Gilberto Ochoa-Ruiz , Thomas Bazin , Luis Falcón-Morales , Dominique Lamarque , Christian Daul

Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs. However, images acquired by an endoscope are frequently affected by illumination artefacts due to the enlightenment source orientation. There exist two major issues when the endoscope’s light source pose suddenly changes: overexposed and underexposed tissue areas are produced. These two scenarios can result in misdiagnosis due to the lack of information in the affected zones or hamper the performance of various computer vision methods (e.g., SLAM, structure from motion, optical flow) used during the non invasive examination. The aim of this work is two-fold: i) to introduce a new synthetically generated data-set generated by a generative adversarial techniques and ii) and to explore both shallow based and deep learning-based image-enhancement methods in overexposed and underexposed lighting conditions. Best quantitative results (i.e., metric based results), were obtained by the deep learning-based LMSPEC method, besides a running time around 7.6 fps.

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An Intelligent Water Consumption Prediction System based on Internet of Things

2020 , Gutiérrez, Sebastián , Ponce, Hiram , Espinosa Loera, Ricardo Abel

This work presents the development of a measurement system for water consumption based on the Internet of Things concept. In this paper, we propose a supervised learning method namely artificial hydrocarbon networks (AHN) to predict water consumption one hour ahead. A Hall effect sensor was used to obtain the water flow value through an embedded system and to show it in an interface developed in Visual Studio. For that, the embedded system sent the data in real time to a database in Firebase using the JSON communication protocol. There, the consumed water flow is stored periodically. Experimental results of the supervised learning model conclude that AHN model predicts the conditions for efficient consumption with an average root-mean squared error of 2.4924 liters per hour. © 2020 IEEE.

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A deep learning-based image pre-processing pipeline for enhanced 3D colon surface reconstruction robust to endoscopic illumination artifacts

2024 , Espinosa Loera, Ricardo Abel , Javier Cerriteño , Saul Gonzalez-Dominguez , Gilberto Ochoa-Ruiz , Christian Daul

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Renewable Energy Prediction through Machine Learning Algorithms

2020 , Luisa Fernanda Jimenez Alvarez , Sebastian Ramos Gonzalez , Antonio Delgado Lopez , Diego Alonso Hernandez Delgado , Espinosa Loera, Ricardo Abel , Gutiérrez, Sebastián

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Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning

2021 , Ponce, Hiram , Cevallos, Claudio , Gutiérrez, Sebastián , Espinosa Loera, Ricardo Abel

Tomato crops are considered the most important agricultural products worldwide. However, the quality of tomatoes depends mainly on the nutrient levels. Visual inspection is made by farmers to anticipate the nutrient deficiency of the plants. Recently, precision agriculture has explored opportunities to automate nutrient level monitoring. Previous work has demonstrated that a convolutional neural network (CNN) is able to estimate low nutrients in tomato plants using images of their leaves. However, the performance of the CNN was not adequate. Thus, this work proposes a novel CNNbased classifier, namely CNN+AHN, for estimating low nutrients in tomato crops using an image of the tomato leaves. The CNN+AHN incorporates a set of convolutional layers as the feature extraction part, and a supervised learning method called artificial hydrocarbon network (AHN) as the dense layer. Different combinations of the architecture of CNN+AHN were examined. Experimental results showed that our best CNN+AHN classifier is able to estimate low nutrients in tomato plants with an accuracy of 95:57% and F1-score of 95:75%, outperforming the literature.

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Click-event sound detection in automotive industry using machine/deep learning

2021 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Gutiérrez, Sebastián

In the automotive industry, despite the robotic systems on the production lines, factories continue employing workers in several custom tasks getting for semi-automatic assembly operations. Specifically, the assembly of electrical harnesses of engines comprises a set of connections between electrical components. Despite the task is easy to perform, employees tend not to notice that a few components are not being connected properly due to physical fatigue provoked by repetitive tasks. This yields a low quality of the assembly production line and possible hazards. In this work, we propose a sound detection system based on machine/deep learning (ML/DL) approaches to identify click sounds produced when electrical harnesses are connected. The purpose of this system is to count the number of connections properly made and to feedback to the employees. We collect and release a public dataset of 25,000 click sounds of 25 ms length at 22 kHz during three months of assembly operations in an automotive production line located in Mexico. Then, we design an ML/DL-based methodology for click sound detection of assembled harnesses under real conditions of a noisy environment (noise level ranging from −16.67 dB to −12.87 dB) including other machinery sounds. Our best ML/DL model (i.e., a combination between five acoustic features and an optimized convolutional neural network) is able to detect click sounds in a real assembly production line with an accuracy of 94.55±0.83 %. To the best of our knowledge, this is the first time a click sounds detection system in assembling electrical harnesses of engines for giving feedback to the workers is proposed and implemented in a real-world automotive production line. We consider this work valuable for the automotive industry on how to apply ML/DL approaches for improving the quality of semi-automatic assembly operations. © 2021 Elsevier B.V.

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Intelligent Management System for Micro-Grids using Internet-of-Things

2021 , Gutiérrez, Sebastián , Medina, Guillermo , Ponce, Hiram , Espinosa Loera, Ricardo Abel

The current research work proposes the design of an intelligent platform for managing the generation, distribution, transmission, commercialization and consumption of electricity, allowing the decision-making process aimed at reducing costs and maximizing the use of energy resources. The technological proposal is a system of information made of integrated sensors. This creates an electrical real-time network that share energy in the cloud through LPWAN networks. Later, the data from the sensors are received in an intelligent platform (Max4 IoT) that employs super-computing systems and artificial intelligence for the analysis of individual and aggregated data. The system is able to learn about the electrical network, knowing the consumers’ behavior and energy trends, allowing to generate responses based on specific situations by sending SMS alerts and emails about abnormal situations or programmed tasks. In addition, the system allows the generation of reports on the network status in real-time and commands control signals on the switching on-and-off of equipment in response to consumption peaks and/or power supply outages. The platform allows interaction with distributed generation systems and micro-grids, through its mobile or web interface, increasing user interaction with the electrical system and the inclusion of renewable energy sources, thus influencing a better quality of service provided to the end user. © 2021 IEEE.

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Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

2020 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Gutiérrez, Sebastián , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , Brieva, Jorge

Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.

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Un enfoque basado en la visión para la detección de caídas utilizando múltiples cámaras y redes neuronales convolucionales: un caso de estudio en UP-Fall Detection Data-Set

2019-12 , Espinosa Loera, Ricardo Abel , HIRAM EREDIN PONCE ESPINOSA;376768 , JOSÉ SEBASTIÁN GUTIÉRREZ CALDERÓN;494470 , Ponce, Hiram , Gutiérrez, Sebastián , Campus Aguascalientes

Actualmente, el reconocimiento automático de caídas humanas es un tema de investigación importante para la visión por computadora y la comunidad de la inteligencia artificial. Para el análisis de imágenes, es común usar un enfoque basado en visión para la detección de caídas y sistemas de clasificación debido al aumento exponencial actual en el uso de cámaras. Además, las técnicas de deep learning han revolucionado las técnicas basadas en visión. Han sido consideradas robustas y confiables en la detección y clasificación de problemas, principalmente usando Redes Neuronales Convolucionales (CNN). Recientemente, nuestro grupo de investigación lanzo un nuevo Data Set multimodal para la detección de caídas (Up-Fall Detecction dataset), y se requieren diferentes estudios de enfoques de modalidades para la detección y clasificación de caídas. Centrándonos solo en un enfoque basado en visión, en este articulo presentamos un sistema de detección de caídas basado en 2D CNN como método de inferencia y varias cámaras. Este enfoque analiza imágenes en marcos de ventana de tiempo fijo que extraen características utilizando un método de flujo óptico que obtiene información de movimiento relativo entre dos imágenes consecutivas. Para resultados experimentales, probamos este enfoque en nuestro dataset público. Los resultados mostraron que nuestra propuesta de enfoque basado en la visión múltiple detecta caídas humanas que alcanzan un 95.64% de precisión con una arquitectura de red CNN simple en comparación con otros métodos de vanguardia.

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