Edge device for movement pattern classification using neural network algorithms

Yauri, Ricardo; Espino, Rafael

doi:10.11591/ijeecs.v30.i1.pp229-236

Cited by 6 publications

(3 citation statements)

References 21 publications

(28 reference statements)

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“…It is necessary to take advantage of the great advances in the field of automatic learning, to predict the behavior of a variable based on previous historical data [12]. For this there are techniques based on embedded artificial intelligence using machine learning [13,14]. This contributes to the design of an intelligent system to predict the occurrence of frosts.…”

Section: Jean Gonzalesmentioning

confidence: 99%

Sprinkler Irrigation Automation System to Reduce the Frost Impact Using Machine Learning

Yauri,

Llerena,

Santiago

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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Frosts reduce the ambient temperature to the freezing point of water, affecting the agricultural sector and the integrity of plant tissues, severely damaged by freezing, destroying plant cells. In addition, losses are generated in the economy due to the death of cattle due to cold, hunger, diseases, etc. Latin America is a region that depends, to a considerable extent, on its crops for its consumption and export, so frost represents an urgent problem to solve, considering that in Perú the area of agriculture is not technical. Among the methods most used by farmers is anticipated irrigation, through automatic learning techniques, which allows predicting the behavior of a variable based on previous historical data. In this paper, sprinkler irrigation is implemented in crops exposed to frost, using an automated system with machine learning techniques and prediction models. Therefore, three types of models are evaluated (linear regression, random forests, and decision trees) to predict the occurrence of frosts, reducing damage to plants. The results show that the protection activation indicator from 1.1°C to 1.7°C was updated to decrease the number of false positives. On the three models evaluated, it is determined that the most accurate method is the Random Forest Regression method, which has 80.91% reliability, absolute mean error, and mean square error close to zero.

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Section: Jean Gonzalesmentioning

confidence: 99%

Sprinkler Irrigation Automation System to Reduce the Frost Impact Using Machine Learning

Yauri,

Llerena,

Santiago

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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“…Currently, the adoption and application of advanced technologies such as digital twins (DT), the Internet of Things (IoT), and augmented reality (AR) are applied in various fields, including the construction industry, education, solar energy, manufacturing, supply chain management, and gastronomy [1] [2] [3]. The importance of Industry 4.0, which encompasses a variety of technologies such as artificial intelligence (AI) integrated with IoT solutions [4] and collaborative robotics, is highlighted to transform and improve processes and services in these sectors [5] [6].…”

Section: Introductionmentioning

confidence: 99%

IoT Control and Visualization System with Digital Twins and Augmented Reality in a Digital Transformation Space

Yauri,

Mallqui

2024

Int. J. Onl. Eng.

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This paper describes the use of Internet of Things (IoT) technologies, digital twins (DT), and augmented reality (AR) to raise awareness and disseminate the use of digital services within the INICTEL-UNI institutional project financed by the Inter-American Development Bank to strengthen technological services and satisfy the technological needs of companies, promoting digital transformation in Peru. Within various fields, such as technical education, construction, and manufacturing, challenges are faced related to the adoption of advanced technologies and the need to improve efficiency. The main objective of this paper is to implement an IoT control and visualization system with DT and AR in a digital transformation space. A system is shown to create a technological demonstrator environment that visualizes and monitors sensor data on physical IoT devices in real time, allowing users to interact and operate them through an ESP32 module with data transmission with the MQTT protocol and an AR application developed in Unity and Vuforia. The study results successfully demonstrated the efficiency of real-time communication between the IoT device and the AR application, as well as the efficient ability to perform tasks, validated by users with no prior experience.

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“…These location processes can be integrated with TinyML and neural networks, designing devices applied to communication and determining the location of objects [8], where the challenge lies in the integration of intelligence techniques in devices with hardware restrictions [9] [10]. In other cases, 5G technology is integrated to contribute to navigation in these environments through 5G reference signals and machine learning [7] [11], while other solutions use RFID technologies [12].…”

mentioning

confidence: 99%

Evaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms

Yauri,

Espino,

Castro

2024

Int. J. Onl. Eng.

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The paper aims to evaluate precise location techniques with indoor devices using edge computing technologies, which are important for services such as smart homes and health. Despite their growing importance, indoor locations lack precise and standard methods, especially in complex environments. Solving this is being attempted through technologies such as reconfigurable surfaces and deep learning models, with attention to overcoming the challenges of indoor placement. The main objective of the study is to design a low-cost indoor location system using the ESP32 module and RSSI signals, integrated with embedded machine learning algorithms. The system to be developed will allow determining the location of objects or people with a location device through SSID signals from access points. The main objective is to evaluate the performance of three machine learning algorithms—random forest (RF), decision tree (DT) and support vector machine (SVM)—in the detection of four different locations (bathroom, kitchen, bedroom, and living room), involving the definition of system characteristics, data acquisition, the development of classifiers, and their integration in the ESP32 module to transmit location data wirelessly through the MQTT protocol. As a result of the evaluation, the DT model stands out for its efficiency under limited resource conditions during real-time implementation, but it may face challenges related to overfitting and resources at the implementation stage.

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Edge device for movement pattern classification using neural network algorithms

Cited by 6 publications

References 21 publications

Sprinkler Irrigation Automation System to Reduce the Frost Impact Using Machine Learning

Sprinkler Irrigation Automation System to Reduce the Frost Impact Using Machine Learning

IoT Control and Visualization System with Digital Twins and Augmented Reality in a Digital Transformation Space

Evaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms

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