IEEE Latin America Transactions

Event-triggered Controlled Charger for Lithium Battery Packs

Javier Blanco Rico — 2024-04-13

Event-triggered Control is a modification for digital based control systems that has gain popularity in recent years. This alteration in control algorithms offers advantages like reductions in computation times or even in the requirements of system's hardware bay reducing its processing and communications to the minimum required; allowing for less computation and communication time needed to perform control actions and making them relevant for environments with shared communication mediums or hardware handling multiple processes simultaneously.

In this work, a comparison is presented between a classic control scheme and its event triggered counterpart when controlling a Power Source for a simple Lithium Battery Charger. The event triggered versions of the current programmed control and the voltage Proportional-Integral-Derivative controllers are designed to adhere to the maximum admissible errors of the system (voltage and current output ripple) and results are presented to show how the modifications manage to obtain equivalent results to thous of their classic continuous counterparts while minimizing control loop actuation and therefore improve computation and communications performance without hindering system efficiency. This results are specially relevant for such ubiquitous devices in complex systems like electric vehicles or portable consumer electronics, allowing for computation time to be dedicated to other tasks and freeing communication channels for other devices.

Evaluation of an AGC for a Solar Panel as a Receiver in a VLC System Applied to Access Control

Simon Valencia Lozano — 2024-04-13

Visible light communication (VLC) technology allows data to be transmitted using the optical power emitted by light-emitting diodes (LEDs). The optical receiver usually employs light sensors such as photodiodes, image sensors, photoresistors or solar panel. In the last decades, VLC technology has explored the use of solar panels as data receivers, since they offer the service of power generation, in addition to, a larger surface area to capture the light beam. In this paper, we present the performance evaluation of a VLC system based on solar panel and automatic gain control (AGC) with application in access control systems. Three experiments are proposed to evaluate the uses of the solar panel as information receiver in VLC systems: bandwidth (BW) estimation, VLC system performance and panel viewing angle variation. As a result, we were able to determine a BW of 10 kHz and 190 kHz considering the panel without AGC and with AGC. In the context of digital modulation, a RS-232 module of an Arduino UNO development system was used in order to execute the technique of intensity modulation and direct detection IM/DD. The NRZ line code of the serial interface was used to modulate the electric current of the LED with a maximum symbol rate of 38400 baud with a data effectiveness of 91%. Furthermore, we found that AGC improves the dynamic range of the solar panel's viewing angle and the performance of the VLC system.

A Proposal of an ECC-based Adaptive Fault-Tolerant Mechanism for 16-bit data words

Joaquín Gracia-Morán — 2024-04-13

With the integration scale level reached in CMOS technology, memory systems provide a great storage capacity, but at the price of an augment in their fault rate. In this way, the probability of experiencing Single Cell Upsets or Multiple Cell Upsets have risen.

Error Correction Codes (ECC) are broadly employed to protect memory systems. Though, the inclusion of an ECC in a computer system adds, in each memory word, some extra bits used to detect and/or correct errors. In addition, encoding and decoding circuitries must be added, introducing overheads in area, delay, and power consumption.

Usually, when an ECC-based fault tolerance mechanism is designed, its fault tolerance properties cannot be modified. However, in some applications, current memory systems can suffer a variable fault rate during their operation. Thus, it seems very interesting that this mechanism would be able to adapt to these variable fault conditions.

This work proposes an Adaptive Fault-Tolerant mechanism based on ECC. This mechanism can adapt to different fault conditions, being able to correct and/or detect single and multiple bits in error. The Adaptive Fault-Tolerant mechanism proposed uses a unique encoder, that is, it is not necessary to re-encode the data to change the error coverage.

Time-series failure prediction on small datasets using machine learning

Caio Souto Maior — 2024-04-13

Condition-based maintenance is a decision-making strategy using condition monitoring information to optimize the availability of operational plants. In this context, machine learning techniques are useful and have been used in predicting the remaining useful life (RUL) of equipment to ensure the overall safety and reliability of the system through maintenance policies and, consequently, reducing costs arising from the failure. These databases are not large which is tricky for data-driven models. In this study, we consider five different databases containing the failure times from distinct real-world equipment. Here, four different regression algorithms were compared for RUL prediction, namely: Support Vector Regression (SVR), Decision Tree (DT), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN). Furthermore, aiming to improve the data quality, the Empirical Mode Decomposition (EMD) was used, which is responsible for pre-processing the input data used on the predictive modeling. We optimize the models’ hyperparameters using grid-search cross-validation algorithm and the performance of each model is compared using the normalized root mean squared error (NRMSE). Considering the datasets analyzed, KNN model proves to be the most promising to perform the prognostic task in small datasets, adapting itself to the distinct characteristics of the different databases. In addition, we mention the better performance after optimizing the hyperparameters, which avoided overfitting problems and had a low computational cost for the problems analyzed here.

A switch placement algorithm to reduce the impact of distribution network maintenance in continuity indexes

Armando Leopoldo Keller — 2024-04-13

The main objective of power distribution utilities is to provide power to their consumers with the smallest number and duration of interruptions, which is why they are evaluated by continuity indexes such as SAIDI and SAIFI. Sometimes, interruptions are needed to expand the network or for maintenance operations. This paper proposes an innovative method to evaluate the best place to install switches based on graph theory and centralities measurements to prevent disconnecting unnecessary customers in a maintenance event. The algorithm is demonstrated step-by-step with a simple example of a theoretical distribution network and then applied to the IEEE 123 bus test case for a comprehensive evaluation

A Technique to Generate Depth Maps from Real Scenes without Manual Calibration

Ricardo S. Casado — 2024-04-13

This paper proposes a technique for the generation of a disparity map from a real scene, captured by a stereo vision system. The underlying motivation for this work is to develop a system not requiring the use of a calibration pattern, which usually involves manual intervention. This is a well desired feature to allow its use in the design of aid devices for people with severe visual impairment or blindness. Experimental results showed that the developed technique has a level of effectiveness similar to other two well established techniques found in the literature, making it a promising alternative to be employed in situations where the calibration step becomes a burden to the user.

Mangrove semantic segmentation on aerial images

Jose Anibal Arias-Aguilar — 2024-04-13

In the Yucatan Peninsula, there is a rich diversity of mangroves, notably including Rhizophora mangle, Avicennia germinans, and Laguncularia racemosa. These mangroves contribute to the recovery of degraded natural areas caused by human activities. Additionally, they serve as natural habitats for various animal and plant species. Studies have highlighted the significance of preserving and restoring these species through traditional methods. More recently, the integration of remote sensing and deep learning techniques has allowed for the automated detection and quantification of mangroves. In this study, we explore the application of deep neural network techniques to address computer vision challenges in the field of remote sensing. Specifically, we focus on the detection and quantification of mangroves in remote image sensing, employing transfer learning and fine-tuning with three distinct deep neural network architectures: SegNet-VGG16, U-Net, and Fully Convolutional Network (R-FCN), with the latter two based on the ResNet network. To evaluate the performance of each architecture, we applied key evaluation metrics, including Intersection over Union (IoU), Dice Coefficient, Precision, Sensitivity, and Accuracy. Our results indicate that SegNet-
VGG16 exhibited the highest levels of Precision (98.03%) and Accuracy (97.03%), while U-Net outperformed in terms of IoU
(96.97%), Dice Coefficient (92.20%), and Sensitivity (96.81%).

Evaluating the 2PC Algorithm for the Maintenance of P2P Live Streaming

Adriel C. dos Santos — 2024-04-13

Peer-to-Peer (P2P) Networks for live streaming face challenges such as ensuring low latency and low discontinuity in media transmission among peers. Algorithms for constructing and maintaining the overlay are often proposed to address several of these challenges. However, it is common to find works that present positive results from the execution of these algorithms without showing the overlay structure constructed by them. In this article, we analyze the overlay constructed and maintained by the Peer Classification for Partnership Constraints (2PC) algorithm. 2PC proved to be efficient in dealing with a large number of free-riders on the network, imposing constraints on partnerships between peers according to their contributions to media transmission. To understand the 2PC execution effects supported by the K-Shortest Path Yen's algorithm, we evaluated the application of the 2PC and identified that the partnership relationships between peers imposed by the algorithm organize the overlay attracting high-contribution peers close to the server, while pushing low-contribution peers to the edge of the overlay.

Parameter Extraction of Two Diode Photovoltaic Model Using An Analytical Approach

Abdul Mujeer Syed — 2024-04-13

In this study, a Two-Diode Model (TDM) is implemented for Photovoltaic (PV) modules to accurately derive PV parameters. A noteworthy contribution of this work is the introduction of a simplified current equation, necessitating the estimation of only seven parameters. Additionally, we present an effective modeling approach for the PV module based on the TDM. The extraction of TDM parameters is accomplished using the suggested analytical method. Case studies are conducted on the PWP-201 module and the R.T.C. France Solar Cell module to showcase the accuracy of the analytical method. Results from an experimental setup comprising two 40 W Poly-crystalline PV panels are also reported. The performance of the proposed analytical method for the TDM is assessed, and its consistency with experimental I-V curves and other methods is established.

Performance study of a photovoltaic system operating on the southeastern coast of Brazil

Júlia de Oliveira Gonzalez — 2024-04-13

The photovoltaic (PV) solar energy generation sector is expanding fast and achieved 23.9 GW of installed power in Brazil in the first months of 2023 (occupying the 2nd place in installed capacity in the Brazilian electricity mix). Such a scenario makes PV power generation a resource for regional development and socioeconomic opportunities for metropolitan regions with a high national economy share and population density. The present work investigated how a PV system’s performance is affected when operating in a coastal area close to high atmospheric particulate and gases emitters like cargo transportation and port operational procedures. The PV system is installed at the Federal University of São Paulo, campus Baixada Santista in Santos/SP, close to Latin America’s largest port in trade operations. Santos is a medium-sized coastal city with a tropical climate, dry winters, and humid summers. The evaluation is based on a database including environmental and operational quantities acquired between October/2020 and September/2021 to calculate the fundamental performance metrics: performance ratio, capacity factor, reference and final yields, and system losses. The performance parameters were analogous to published results achieved by PV systems worldwide in coastal and continental areas. The PV system performance at Unifesp’s Building rooftop was not affected by regional climate and local atmospheric conditions related to the nearby polluting economic activities, including emissions of gases and particulates into the atmosphere. Finally, the PV system showed better performance than that simulated by the SOLergo tool regarding the generated energy, probably due to the influence of local environmental variables and careful operational maintenance not considered in the numerical simulations.

Table of Contents May 2024

Daniel Ulises Campos Delgado — 2024-04-13

Table of Contents May 2024