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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Paiva-Peredo, Ernesto
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Design of a prototype for sending fire notifications in homes using fuzzy logic and internet of things Huaman Castañeda, Johan; Tamara Perez, Pablo Cesar; Paiva-Peredo, Ernesto; Zarate-Segura, Guillermo
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp248-257

Abstract

This paper highlights the need to address fire monitoring in densely populated urban areas using innovative technology, in particular, the internet of things (IoT). The proposed methodology combines data collection through sensors with instant notifications via text messages and images through the user’s email. This strategy allows a fast and efficient response, with message delivery times varying from 1 to 4 seconds on Internet connections. It was observed that the time to send notifications on 3G networks is three times longer compared to Wi-Fi networks, and in some 3G tests, the connection was interrupted. Therefore, the use of Wi-Fi is recommended to avoid significant delays and possible bandwidth issues. The implementation of fuzzy logic in the ESP32 microcontroller facilitates the identification of critical parameters to classify notifications of possible fires and the sending of evidence through images via email. This approach successfully validated the results of the algorithm by providing end users with detailed emails containing information on temperature, humidity, gas presence and a corresponding image as evidence. Taken together, these findings support the effectiveness and potential of this innovative solution for fire monitoring and prevention in densely populated urban areas.
Comparison of algorithms for the detection of marine vessels with machine vision Rodríguez-Gonzales, José; Niquin-Jaimes, Junior; Paiva-Peredo, Ernesto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6332-6338

Abstract

The detection of marine vessels for revenue control has many tracking deficiencies, which has resulted in losses of logistical resources, time, and money. However, digital cameras are not fully exploited since they capture images to recognize the vessels and give immediate notice to the control center. The analyzed images go through an incredibly detailed process, which, thanks to neural training, allows us to recognize vessels without false positives. To do this, we must understand the behavior of object detection; we must know critical issues such as neural training, image digitization, types of filters, and machine learning, among others. We present results by comparing two development environments with their corresponding algorithms, making the recognition of ships immediately under neural training. In conclusion, it is analyzed based on 100 images to measure the boat detection capability between both algorithms, the response time, and the effectiveness of an image obtained by a digital camera. The result obtained by YOLOv7 was 100% effective under the application of processing techniques based on neural networks in convolutional neural network (CNN) regions compared to MATLAB, which applies processing metrics based on morphological images, obtaining low results.
Mechatronic system to classify plastic and metal bottles using capacitive and inductive sensors Melo, Napoly; Gonzales, Abigail Sanchez; Paiva-Peredo, Ernesto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4970-4976

Abstract

The problem addressed in this article focuses on the management of plastic waste, which has experienced a significant increase in recent years, posing challenges in its management and recycling. In addition, the concentration of microplastics in water and their impact on health and the food chain is highlighted. The proposed solution consists of developing a mechatronic system for sorting plastic and metal bottles using capacitive and inductive sensors, respectively. The system demonstrated efficiency in tests, achieving 100% sorting for plastic and metal bottles. The need for bottles to be properly positioned for optimal performance was identified. This work highlights the importance of automation in mechatronic systems and the effectiveness of capacitive and inductive sensors in sorting materials.
Impact of start-stop systems on motorcycle fuel savings in urban traffic Murga-Garcia, Kevin; Chacaltana-Silva, Rodrigo; Paiva-Peredo, Ernesto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6258-6264

Abstract

The start/stop (S/S) system implemented in motorcycles aims primarily at fuel savings. This study was conducted to assess the effectiveness of this system in conditions of heavy traffic and traffic lights in Lima, using a virtual channel identifier (VCI) and a technical schedule. The detailed analysis covered critical aspects of the S/S system, the description of the route taken, and its segmentation to understand the number of stops and mileage. Speed limits, schedules, and measurement equipment were established, including the MICODUS-ORBD2 device and the VCI-Hero. The study included tests conducted with and without the MICODUS-ORBD2 device, recording times, distances, and fuel consumption. Data were collected with the S/S activated and deactivated, concluding the system achieves a 10.1% fuel saving. This finding provides valuable insights into understanding the system's effectiveness in actual traffic conditions and emphasizes the importance of maintaining key vehicle components to optimize S/S performance.
An automated power of hydrogen controlled filtration system for enhanced aquarium fish farming Garcia, Fabio; Martel, Daniel; Paiva-Peredo, Ernesto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6265-6270

Abstract

The increasing popularity of fish keeping in aquariums and the need for electronic equipment to maintain an optimal environment. This article focuses on monitoring water purity to ensure fish health and longevity, addressing the issue of water pollution caused by chemicals and waste produced by fish. Solutions such as mechanical and biological filters are explored, highlighting the use of the mechanical filter composed of zeolite, ceramic rings, and activated carbon, which work to remove solid particles, toxic compounds, and pollutants from the aquarium water. The article presents the implementation of a mechanical filter controlled by a PIC18F4550 microcontroller using a pH sensor. The results indicate the stability of the pH of the water in the established range of 6.5 to 7.5, with a maximum error of 3% at the upper limit of the range and no error below the established lower limit. It is concluded that the system effectively maintains the desired levels and ensures the fish's health. A technological solution for monitoring and controlling water quality is presented, recognizing the possibility of improvements in aquaculture.
Transformations for non-destructive evaluation of brix in mango by reflectance spectroscopy and machine learning Paiva-Peredo, Ernesto; Gonzales-Rodriguez, Diego; Trujillo Herrera, William; Soria Quijaite, Juan Jesús; Quispe-Arpasi, Diana; Paulino, Christian Ovalle
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp532-546

Abstract

Mango is a very popular climacteric fruit in America and Europe. Among the internal properties of the mango, total soluble solids (TSS) are an adequate indicator to estimate the quality of mango, however, the measurement of this indicator requires destructive tests. Several research have addressed similar issues; they have made use of pre-processing transformations without making it clear which of them is statistically better. Here, we created a new spectral database to build machine learning (ML) models. We analyzed a total of 18 principal component regression (PCR) models and 18 partial least squared regression (PLSR) models, where 4 types of transformations, 3 different feature extractors, and 3 different pre-processing techniques are combined. The research proposes a double cross validation (CV) both to determine the optimal number of components and to obtain the final metrics. The best model had a root mean square error (RMSE) of 1.1382 °Brix and a RMSE on the transformed scale of 0.5140. The best model used 4 components, used y2 transformation, reflectance R as the independent variable and MSC as a pre-processing technique.
Management and monitoring of lithium-ion battery recharge with ESP32 Gomez-Huaylla, Estefany; Mejía-Cruz, Luis; Paiva-Peredo, Ernesto
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1677-1686

Abstract

Air quality is important for human health, the use of clean energy is one way to improve it. And the management and monitoring of the recharge of ion-lithium batteries used in electric vehicles and other devices requires efficient systems. The objective is to develop an intelligent electrical recharging system for lithium-ion batteries using internet of thing (IoT) technology. In this article, an electrical recharging system for lithium-ion batteries was designed and carried out, which is made up of a source, a diode bridge, L298 n driver, current sensor, a voltage divider sensor and the ESP32 microcontroller. The system determines the storage capacity of the battery and monitor it remotely via WIFI. The data is sent to a Shiftr.io server and graphically displayed on a NODE RED platform. The message queuing telemetry transport (MQTT) protocol is used to communicate the devices and decide the best time to recharge the batteries. The results show that the system works correctly and offer useful information that optimizes the charging process, it contributes to improving savings in the payment of electricity consumption and the use of clean energy. The limitations of the study are the small sample size and the lack of comparison with other similar systems.
Internet of things and radio frequency identification based embedded system to reduce shopping time in supermarkets Espino, Cesar Solis; Vargas, Favio Guerrero; Paiva-Peredo, Ernesto; Segura, Guillermo Wenceslao Zarate
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7343

Abstract

Doing daily shopping in a Peruvian supermarket means a large investment of time for many people, usually due to inaccurate and faulty scanning of products by barcodes at supermarket checkout counters. For this reason, an embedded system based on internet of things (IoT) and radio frequency identification (RFID) is designed to reduce shopping time in a supermarket. The system uses an ESP32 development board with embedded hardware specialized in IoT projects and firmware development based on C language and real-time operating systems (FreeRTOS) through espressif’s IoT development framework (ESP-IDF). RFID tags were used to scan the products and IoT with message queuing telemetry transport (MQTT) communication protocol are implemented to a local database in real time. The system achieves a significant reduction in terms of scanning time compared to self-service checkouts using barcodes, which allows to statistically analyze the reduced time per quantity of products and the linear trend of the 2 samples.
Security in smart cities using YOLOv8 to detect lethal weapons Rodriguez-Rosas, Ederson; Castillo-Turpo, Aron; Acuna-Condori, Kevin; Paiva-Peredo, Ernesto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp945-953

Abstract

The increase in the illegal use of lethal weapons at a global level has reachedworrying figures, resulting in an increase in assaults and armed robberies. Based on the above, closed circuit television (CCTV) surveillance systems emerge as an alternative solution. Therefore, the use of artificial intelligence is explored in order to detect the presence of lethal weapons in images accurately. In this study, a convolutional neural network model YOLOv8 is trained. A database including 4104 images with the presence of lethal weapons is generated. The Google Colab platform is used for the training phase, since it provides us with a free graphic processing unit (GPU), and the YOLOv8x and YOLOv8n models are used for comparison. The results indicate an advantage when using the YOLOv8 models, and when comparing them with similar models already proposed in the studied literature, we can conclude that our model stands out with an accuracy of 89.56% in the detection of lethal weapons. In conclusion, we were able to obtain a model capable of detecting lethal weapons in CCTV images, in addition to being able to be used in applications that require real-time detection. 
Real-time age-range recognition and gender identification system through facial recognition Cruz-Colan, Carlos; Lopez-Herrera, David; Paiva-Peredo, Ernesto; Acuna-Condori, Kevin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp992-999

Abstract

Facial recognition and age estimation are being implemented in apparel retailing which is undergoing significant changes due to fashion and technology. To improve interaction with customers and refine marketing strategies. The paper proposes an approach based on a Siamese neural network and the use of tools such as MediaPipe for face detection and DeepFace for age and gender estimation. In addition, the four stages of the research work, real-time image capture, ID assignment, facial feature extraction, and data storage, are described. Early approaches to age estimation were based on biometric features, such as eyes, nose, mouth, and chin, resulting in limited accuracy and low performance in older adults. To improve accuracy, additional elements, such as the presence of wrinkles, were considered and a diverse database of images was used. The proposed methodology achieves a positive result for real-time age classification and gender ID. The results include information on gender, age, ID, time and date for each person identified.
Co-Authors Acuna-Condori, Kevin Castillo-Turpo, Aron Chacaltana-Silva, Rodrigo Cruz-Colan, Carlos Espino, Cesar Solis Garcia, Fabio Gomez-Huaylla, Estefany Gonzales, Abigail Sanchez Gonzales-Rodriguez, Diego Huaman Castañeda, Johan Lopez-Herrera, David Martel, Daniel Mejía-Cruz, Luis Melo, Napoly Murga-Garcia, Kevin Niquin-Jaimes, Junior Paulino, Christian Ovalle Quispe-Arpasi, Diana Rodríguez-Gonzales, José Rodriguez-Rosas, Ederson Segura, Guillermo Wenceslao Zarate Soria Quijaite, Juan Jesús Tamara Perez, Pablo Cesar Trujillo Herrera, William Vargas, Favio Guerrero Zarate-Segura, Guillermo