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Rafael Espino
Universidad Tecnológica del Perú

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Embedded electronic system for evaluation of photovoltaic modules based on a current-voltage curve tracer Ricardo Yauri; Rafael Espino
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1281-1289

Abstract

The rapid growth of the market for the use of renewable energy has increased the use of solar energy which has a significant role in power generation. This requires the insertion of equipment capable of providing precise measurements of the photovoltaic modules, either to verify the operation of the installation or to find specific problems. In this scenario, the current versus voltage curve tracer is used to describe the electrical behavior of the photovoltaic system through all the operating possibilities, but it has an excessive cost for small installations. This paper presents the development of a current-voltage curve tracer, capable of performing current, voltage and power measurements, contributing to the creation of equipment to test photovoltaic installations. The methods to obtain the I-V curves are presented and the characteristics of the embedded electronic system, which is based on an electronic load, are defined. As results, the simulations carried out for the variable load control, acquisition circuits and the implemented system are shown. In addition, the operation of the human-machine interface and the comparison with a commercial equipment are shown for reference.
Implementation of a sensor node for monitoring and classification of physiological signals in an edge computing system Ricardo Yauri; Antero Castro; Rafael Espino; Segundo Gamarra
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp98-105

Abstract

We describe the design and development of sensor nodes, based on Edge computing technologies, for the processing and classification of events detected in physiological signals such as the electrocardiographic signal (ECG is the electrical signal of the heart), temperature, heart rate, and human movement. The edge device uses a 32-bit Tensilica microcontroller-based module with the ability to transmit data wirelessly using Wi-Fi. In addition, algorithms for classification and detection of movement patterns were implemented to be implemented in devices with limited resources and not only in high-performance computers. The Internet of Things and its application in smart environments can help non-intrusive monitoring of daily activities by implementing support vector machine (SVM is a machine learning algorithm) for implementation in embedded systems with low hardware resources. This paper shows experimental results obtained during the acquisition, transmission, and processing of physiological signals in a edge computing system and their visualization in a web application.
Edge device for movement pattern classification using neural network algorithms Ricardo Yauri; Rafael Espino
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp229-236

Abstract

Portable electronic systems allow the analysis and monitoring of continuous time signals, such as human activity, integrating deep learning techniques with cloud computing, causing network traffic and high energy consumption. In addition, the use of algorithms based on neural networks are a very widespread solution in these applications, but they have a high computational cost, not suitable for edge devices. In this context, solutions are created that bring data analysis closer to the edge of the network, so in this paper models adapted to an edge device for the recognition of human activity are evaluated, considering characteristics such as inference time, memory, and precision. Two categories of models based on deep and convolutional neural networks are developed by implementing them in C language and comparing with the TensorFlow Lite platform. The results show that the implementations with libraries have a better accuracy result of 76% using principal component analysis inputs, obtaining an execution time of 9ms. Therefore, when evaluating the models, we must not only consider their accuracy but also the execution time and memory on the device.
Co-Authors Antero Castro Ricardo Yauri Ricardo Yauri Ricardo Yauri Segundo Gamarra