IEEE Latin America Transactions

A Systematic Review of Radio Wave Techniques for Indoor Positioning Systems

2024-12-22T04:41:22-08:00

Indoor human positioning has become crucial for applications such as health monitoring, security surveillance, human pose identification and rescue operations. However, achieving reliable indoor human positioning is challenging due to numerous constraints.This paper aims to compare and analyze radio waves techniques and approaches for indoor positioning,
providing a comprehensive review for human detection, positioning and activity recognition. A systematic review of the scientific literature and datasets was conducted. Four digital libraries, ACM Library Digital, IEEE Xplore, ScienceDirect and Spring Link were searched to identify results that met the selection criteria. A data extraction process was performed on the selected articles and datasets. The Parsifal platform was utilized to extract relevant information. After completing the systematic review, It was identified 26 eligible articles and extracted 11 methods for radio wave detection. The overview of indoor positioning system with radio waves was introduced. The most frequently mentioned tools in the articles for the capture stage were Radar Sensors, Wireless Sensor, and Antennas. For the processing stage, DNN Techniques, Processing Algorithms followed by Filtering, Fingerprint, Trilateration, and other machine learning algorithms formed the majority.

A Memetic Genetic Particle Swarm Optimization for Druglike Molecule Discovery

2024-11-28T06:59:57-08:00

Given the vast and complex chemical search space, developing new techniques for identifying promising ligands that satisfy multiple objectives is highly desirable to reduce the costs and times required for effective drug discovery. Neural networks are frequently employed for this task, but they tend to generate molecules that are invalid both chemically and syntactically. As an alternative, metaheuristics have emerged as promising approaches, delivering notable results with reasonable computational costs. However, they often suffer from information loss during the process, leading to poor quality generations. In this work, we introduce a novel memetic algorithm that hybridizes Particle Swarm Optimization with Simulated Annealing. This approach aims to improve the balance between exploration and exploitation in the de-novo drug discovery process, ensuring that promising molecules are not overlooked during generation steps. We compare our approach against six state-of-the-art algorithms, and the results demonstrate that our algorithm enhances molecule generation quality, showing an increased diversity and improved chemical properties of the resulting ligands.

A comparative study between deep learning approaches for aphid classification

2024-11-19T17:13:32-08:00

This study presents a performance comparison between two convolutional neural networks in the task of detecting aphids in digital images: AphidCV, customized for counting, classifying, and measuring aphids, and YOLOv8, state-of-the-art in real-time object detection. Our work considered 48,000 images for training for six different insect species (8,000 images divided into four classes), in addition to data augmentation techniques. For comparative purposes, we considered evaluation metrics available to both architectures (Accuracy, Precision, Recall, and F1-Score) and additional metrics (ROC Curve and PR AUC for AphidCV; mAP@50 and mAP@50-95 for YOLOv8). The results revealed an average F1-Score=0.891 for the AphidCV architecture, version 3.0, and an average F1-Score=0.882 for the YOLOv8, medium version, demonstrating the effectiveness of both architectures for training aphid detection models. Overall, AphidCV performed slightly better for the majority of metrics and species in the study, serving its design purpose very well. YOLOv8 proved to be faster to converge the models, with the potential to apply in research considering many aphid species.

Convolutional Neural Networks using the SMOTE Algorithm and Features Fusion for Wind Turbine Fault Prediction

2024-12-08T03:59:46-08:00

This research introduces an innovative method using Convolutional Neural Networks (CNNs) to identify mass imbalances in wind turbine rotors through a feature fusion strategy. To address the issue of class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is applied. A detailed simulation was carried out using a 1.5 MW three-bladed Wind Turbine model, employing tools such as Turbsim, FAST, and Matlab Simulink, to collect rotor speed data under different wind conditions. Mass imbalances were simulated by modifying blade density in the software. The fusion architecture combines feature extraction with Power Spectral Density analysis, improving the CNN’s ability to work across both frequency and time domains. The effectiveness of this approach was confirmed through a comparative analysis with 9 classifiers and 4 different dataset combinations, demonstrating its capability in detecting mass imbalances.

UAV Flight Comparison Using Backstepping: On-board Data and Observers

2024-12-25T06:59:27-08:00

This paper presents the comparison of performance in flight trajectory tracking for a commercial UAV AR.Drone 2.0, using state observers and on board data. This work seeks to establish that state observers are an alternative to close the control loop in this type of applications. The control strategy proposed for the flight is designed using the Backstepping technique. For the implementation of the control law, knowledge of the position and orientation of the UAV is assumed, therefore, its longitudinal and rotational velocities are estimated either by using observers or data from the combination of inertial and visual measurement of the on-board sensors. In both cases, the designed control strategy makes the UAV converge to the preestablished flight trajectory. However, an analysis of the mean square error between the UAV trajectory with respect to the desired trajectory, gives as a result that, in three of the four compared states, the error obtained with the observer is lower. The theoretical results presented are validated experimentally.

Innovative approach for the analysis of electromagnetic transients generated by the integration of distributed energy resources in power systems

2024-11-16T13:22:40-08:00

This article presents a set of methods for the extraction of features from transient events in power systems, using EMT (Electromagnetic Transients) simulations. The main approach is based on the application of the wavelet transform to transient signals, allowing for the analysis and characterization of various types of transient events. Based on the information obtained from the wavelet transform, specific methods such as impedance analysis, energy, peaks, and selectivity indices are developed and applied. These methods provide useful tools for the identification and classification of transient events, constituting a basis for the characterization of various types of disturbances in power systems. The techniques developed enabled the extraction of unique and distinguishing features of photovoltaic resources, which can later be applied to non-intrusive monitoring of these systems.

Hybrid Attack Optimization Supported Enhanced Deep Learning to Facilitate Power System Event Detection using PMU Data

2024-12-10T04:32:27-08:00

Accurate event detection is crucial for initiating control and protection measures in power systems to ensure enhanced and reliable operation. Phasor measurement units (PMUs) play a vital role in various functional aspects of power systems, including state estimation and intelligent protection algorithms. However, the authenticity of real-time data from PMUs must be verified before feeding it into applicable real-time algorithms to prevent undesirable or erroneous operations. This paper aims to present an efficient preprocessing methodology for identifying unwanted, incorrect, missing, or noisy PMU data to facilitate robust event detection algorithms. The proposed methodology leverages real-time data-driven deep learning techniques for authenticating incoming data. Given the high sampling rate of PMUs, the presence of extraneous data can lead to false event detection, necessitating reliable data preprocessing. Challenges identified in existing literature, such as the limitations of Steady State (SS)-Local Outlier Factor (LOF) in event detection and classification, issues with detecting line tripping and inter-area oscillations, computational and bandwidth requirements for micro-PMU installations, and false alarms resulting from inaccuracies in frequency ramp rate determination, are addressed. To overcome these challenges, this research proposes a deep learning approach that utilizes modified Deep Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) classifiers to classify and extract features from PMU data, enabling highly efficient detection of disturbances in transmission lines. Additionally, a hybrid attack optimization (HAO) technique is employed to enhance convergence rates, accuracy, and efficiency. Performance evaluation of the proposed system is conducted by calculating and assessing disturbances generated by power lines using metrics such as accuracy, precision, recall, System Average Interruption Duration Index (SAIDI), and System Average Interruption Frequency Index (SAIFI).

Optimal Integration of EV Charging Stations and Capacitors for Net Present Value Maximization in Distribution Network

2024-11-25T02:40:54-08:00

The widespread adoption of electric vehicles (EVs) is crucial for reducing greenhouse gas emissions from traditional vehicles. Central to this adoption is the strategic deployment of electric vehicle charging stations (EVCS), whose improper positioning can pose challenges to electrical networks and utility operators. This paper introduces a novel hybrid approach for optimizing the placement of EVCS and capacitors (CAP) in the distribution network (DN) to mitigate active power loss (APL) and enhance operational efficiency. The methodology includes the optimal placement of CAP banks and EVCS across the network, which is evaluated using the Net Present Value (NPV) criterion. Additionally, the study comprehensively considers the integration of vehicle-to-grid (V2G) capabilities, enhancing network
reliability. The proposed hybrid algorithm combines the genetic algorithm (GA) and particle swarm optimization (PSO), i.e., HGAPSO, which leverages their respective strengths in exploration and exploitation. A comprehensive sensitivity analysis is conducted for the IEEE 33, 69, 85, 118, and Brazil 136- bus systems, focusing on cost variables such as energy prices, maintenance costs, and system parameters. This analysis further validates the robustness of the proposed approach, demonstrating
significant reductions in APL and maximization of net profit. Comparative results verify the superiority of the hybrid approach over conventional GA and PSO in optimizing the locations of charging stations and reactive power sources within networks.

Agreement analysis of heart rate variability indices at two different sampling rates for monitoring applications.

2024-11-16T11:40:13-08:00

Beat-to-beat variations in heart rate lead to heart rate variability (HRV), analysed from electrocardiogram or photoplethysmography signals, forming a non-equispaced time series of beats, which requires a resampling of 3 Hz or 4 Hz, for analysis in the frequency domain. HRV is considered a biomarker, predictor of the evolution of diseases in intensive care units (ICU). To enhance these HRV studies, it is necessary to monitor the patient’s health using portable devices, from admission to the ICU until discharge from it and subsequently at home. This requires monitoring devices that can minimise energy consumption and data storage. Reducing the sampling frequency in HRV can reduce energy consumption, computing power and to limite data storage. Therefore, the objective of this work is to prove that a series resampled at 1 Hz allows obtaining HRV indices, equivalent to a 3 Hz. Through concordance analysis, using a database of subjects with pharmacological autonomic blockade and postural changes. The results show equivalences between the indices, standard deviation (SDNN), total spectral power (PT), low frequency (LF) and long-term variability (SD2) and agree with those reported as predictors. This study has limitations, since only a small number of young men participated. Future studies should consider this. The reduction of SDNN, PT, LF values would be predictors of mortality in hospitals, so the equivalence found from series with 1Hz resampling, would allow the use of portable devices with optimized performance, to monitor the evolution of the disease in patients in ICUs.

An Alternative Design of a Compact and Portable Six-Port Reflectometer for 2.4 GHz Reflection Coefficient Measurements

2024-12-09T10:42:14-08:00

In this article, the design and implementation of a compact and portable six-port reflectometer for measuring the reflection coefficient at 2.4 GHz is presented. The proposed design utilizes reduced-size components, such as an RF generator and an acquisition board, achieving a portable solution that can be used outside the RF laboratory. In this design, the six-port circuit consists of only two microstrip directional couplers with an angularly equidistant distribution of their q points. This configuration facilitates the implementation of the reflectometer, reducing complexity without sacrificing accuracy, even when compared to a commercial VNA. This design is an attractive and efficient alternative for research projects requiring the use of six-port reflectometry technique.

Table of Contents March 2025

2025-01-04T02:38:27-08:00

Table of Contents March 2025