Espinosa Loera, Ricardo Abel

Main Affiliation
Preferred name
Espinosa Loera, Ricardo Abel
Official Name
Espinosa Loera, Ricardo Abel
ORCID
0000-0003-2573-7853
Scopus Author ID
57211517018

Settings

Now showing 1 - 10 of 20
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
      15Scopus© Citations 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
      18
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
      13Scopus© Citations 1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Click Event Sound Detection Using Machine Learning in Automotive Industry
    (2020)
    Espinosa Loera, Ricardo Abel  
    ;
    Ponce, Hiram  
    ;
    Gutiérrez, Sebastián
    ;
    Hernández Cornu, Javier Eluney
    Artificial intelligence has been playing an important role when it comes to the automotive industry and its quality of assemblies in the production line, this is because since the arrival of the industry 4.0 it has been subject to change and continuous improvement. In the past, we’ve observed how many machine learning architectures have been used to create environmental sound classification systems in order to improve traditional systems, thus overcoming efficiency issues with great results. In this work, we present a machine learning solution/approach for click event sound detection using audio sensors that are used in the assembly of electric harnesses for engines, this being done on an automotive production line, where we divided our workflow into: data collection, pre-processing, feature extraction, training and inference and finally the detection of the click event sounds. We created a dataset that is composed by 25,000 audio files that have an average duration of 0.025 seconds per click sound with the purpose of training a Multi-layer Perceptron and bring it into the inference phase. In order to test this approach, we’ve performed various implementations in a laboratory and in the real automotive industry. We obtained 95.23% in F1-Score Metric in a laboratory, while in real conditions, we obtained less reliable results, as 84.00% as the best results. © 2020, Springer Nature Switzerland AG.
      2  11Scopus© Citations 1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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  
    Scopus© Citations 10  1  9
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Prompt Assisted Enhancement for Correcting Illumination Artifacts in Endoscopic Images
    (Springer Nature Switzerland, 2025-10-24)
    Espinosa Loera, Ricardo Abel  
    ;
    Eluney Hernández
    ;
    Javier Cerriteño Magaña
    ;
    Gilberto Ochoa
    ;
    Christian Daul
      13
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
      2Scopus© Citations 31  18
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
      1  15Scopus© Citations 1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
    (2023)
    Axel García-Vega
    ;
    Espinosa Loera, Ricardo Abel  
    ;
    Luis Ramírez-Guzmán
    ;
    Thomas Bazin
    ;
    Luis Falcón-Morales
      2  35Scopus© Citations 13
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
      2  31Scopus© Citations 39