Varela Aldas, José
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Varela Aldas, José
Main Affiliation
Ambato
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josevarela@uti.edu.ec
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Scopus Author ID
57192546163

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Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models

2025 , Pedro Torres-Bermeo , Kevin López-Eugenio , Carolina Del-Valle-Soto , Guillermo Palacios-Navarro , Varela Aldas, José

The efficient sizing and characterization of the load curves of distribution transformers are crucial challenges for electric utilities, especially given the increasing variability of demand, driven by emerging loads such as electric vehicles. This study applies clustering techniques and predictive models to analyze and predict the behavior of transformer demand, optimize utilization factors, and improve infrastructure planning. Three clustering algorithms were evaluated, K-shape, DBSCAN, and DTW with K-means, to determine which one best characterizes the load curves of transformers. The results show that DTW with K-means provides the best segmentation, with a cross-correlation similarity of 0.9552 and a temporal consistency index of 0.9642. For predictive modeling, supervised algorithms were tested, where Random Forest achieved the highest accuracy in predicting the corresponding load curve type for each transformer (0.78), and the SVR model provided the best performance in predicting the maximum load, explaining 90% of the load variability (R2 = 0.90). The models were applied to 16,696 transformers in the Ecuadorian electrical sector, validating the load prediction with an accuracy of 98.55%. Additionally, the optimized assignment of the transformers’ nominal power reduced installed capacity by 39.27%, increasing the transformers’ utilization factor from 31.79% to 52.35%. These findings highlight the value of data-driven approaches for optimizing electrical distribution systems.

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Depth-Enhanced Tumor Detection Framework for Breast Histopathology Images by Integrating Adaptive Multi-Scale Fusion, Semantic Depth Calibration, and Boundary-Guided Detection

2025 , A. Robert Singh , Suganya Athisayamani , Hariharasitaraman S , Faten Khalid Karim , Varela Aldas, José , Samih M. Mostafa

Multiple modalities offer the advantage of providing complementary visual information that enhances overall understanding. This capability is particularly valuable in the domain of autonomous tumor detection. However, the challenge of occlusions in autonomous tumor perception—especially in cases of tumor-to-tumor occlusion—hinders the effective utilization of occlusion-related features. As a result, these limitations lead to a decline in the accuracy of object detection. We propose a novel framework for tumor detection in histopathological images by integrating multi-scale RGB features and depth-enhanced semantic information. The method comprises three primary modules: the Adaptive Multi-Scale Fusion Module (AMSF), Semantic Depth Integration and Calibration Module (SDICM), and Depth-Guided Tumor Detection Module (DG-TDM). AMSF combines RGB histopathology image channels, binary masks, and multi-scale convolution outputs using attention mechanisms to generate a fused feature map that captures occlusion information and boundary relevance. SDICM constructs dense depth maps by fusing RGB, semantic, and sparse depth data through bidirectional feature aggregation, enhancing spatial continuity and edge clarity. DG-TDM refines tumor boundary detection by combining depth and RGB features using spatial and channel-wise attention mechanisms, which highlight boundary differences and reduce redundancy from background noise. The proposed loss function optimizes RGB-depth feature alignment, balancing contributions from each modality for robust tumor detection. This approach addresses challenges such as overlapping boundaries, occlusions, and poor contrast in histopathology images, enabling precise localization of tumor regions. Experimental results demonstrate that the proposed framework significantly improves detection accuracy by 98% and boundary delineation compared to existing methods. This robust and efficient methodology provides a promising solution for advancing tumor detection in medical imaging.

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Visual Servoing Using Sliding-Mode Control with Dynamic Compensation for UAVs’ Tracking of Moving Targets

2024 , Christian P. Carvajal , Víctor H. Andaluz , Varela Aldas, José , Flavio Roberti , Carolina Del-Valle-Soto , Ricardo Carelli

An Image-Based Visual Servoing Control (IBVS) structure for target tracking by Unmanned Aerial Vehicles (UAVs) is presented. The scheme contains two stages. The first one is a sliding-model controller (SMC) that allows one to track a target with a UAV; the control strategy is designed in the function of the image. The proposed SMC control strategy is commonly used in control systems that present high non-linearities and that are always exposed to external disturbances; these disturbances can be caused by environmental conditions or induced by the estimation of the position and/or velocity of the target to be tracked. In the second instance, a controller is placed to compensate the UAV dynamics; this is a controller that allows one to compensate the velocity errors that are produced by the dynamic effects of the UAV. In addition, the corresponding stability analysis of the sliding mode-based visual servo controller and the sliding mode dynamic compensation control is presented. The proposed control scheme employs the kinematics and dynamics of the robot by presenting a cascade control based on the same control strategy. In order to evaluate the proposed scheme for tracking moving targets, experimental tests are carried out in a semi-structured working environment with the hexarotor-type aerial robot. For detection and image processing, the Opencv C++ library is used; the data are published in an ROS topic at a frequency of 50 Hz. The robot controller is implemented in the mathematical software Matlab.

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Combining Image Classification and Unmanned Aerial Vehicles to Estimate the State of Explorer Roses

2024 , David Herrera , Pedro Escudero-Villa , Eduardo Cárdenas , Marcelo Ortiz , Varela Aldas, José

The production of Explorer roses has historically been attractive due to the acceptance of the product around the world. This species of roses presents high sensitivity to physical contact and manipulation, creating a challenge to keep the final product quality after cultivation. In this work, we present a system that combines the capabilities of intelligent computer vision and unmanned aerial vehicles (UAVs) to identify the state of roses ready for cultivation. The system uses a deep learning-based approach to estimate Explorer rose crop yields by identifying open and closed rosebuds in the field using videos captured by UAVs. The methodology employs YOLO version 5, along with DeepSORT algorithms and a Kalman filter, to enhance counting precision. The evaluation of the system gave a mean average precision (mAP) of 94.1% on the test dataset, and the rosebud counting results obtained through this technique exhibited a strong correlation (R2 = 0.998) with manual counting. This high accuracy allows one to minimize the manipulation and times used for the tracking and cultivation process.

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Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks

2025 , Carolina Del-Valle-Soto , Leonardo J. Valdivia , Ramiro Velázquez , José A. Del-Puerto-Flores , Varela Aldas, José , Paolo Visconti

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Influence of Higher Education on IoT Acceptance through Hands-On Learning

2025 , Varela Aldas, José , Christian Junta , Elias Choque , Guillermo Palacios-Navarro

The Internet of Things (IoT) applications are pervasive across various sectors; however, there remains some resistance to its adoption. Education 4.0 promotes the full integration of new technologies, both as tools for learning and instruments for professional development. This work studies the influence of higher education on the willingness towards IoT adoption after hands-on learning experiences. The primary objective is to determine whether a correlation exists between IoT adoption and the education of university students from three distinct professional degrees. The methodology employed involves a practical class where students engage in developing applications for manual data collection. These applications are designed to send data to the Internet, which is then visualized through a web interface. Tailored to each respective degree, three similar applications are developed. For this research, M5 Stack Core2 kits are utilized, along with UIFLOW programming language and the ThingSpeak platform, operating under the MQTT protocol. Following the training, students complete a Technology Acceptance Model (TAM) survey for IoT. The analysis of the influence of higher education on IoT acceptance employs ANOVA to identify differences between group means. The results reveal statistically significant differences in IoT acceptance between students in Industrial and Architecture degrees.

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Visual Servoing NMPC Applied to UAVs for Photovoltaic Array Inspection

2024 , Edison Velasco-Sánchez , Luis F. Recalde , Bryan S. Guevara , Varela Aldas, José , Francisco A. Candelas , Santiago T. Puente , Daniel C. Gandolfo

The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on the capture and detailed analysis of aerial images (photogrammetry). However, the photogrammetry approach presents limitations, such as an increased amount of useless data and potential issues related to image resolution that negatively impact the detection process during high-altitude flights. In this work, we develop a visual servoing control system with dynamic compensation using nonlinear model predictive control (NMPC) applied to a UAV. This system is capable of accurately tracking the middle of the underlying PV array at various frontal velocities and height constraints, ensuring the acquisition of detailed images during low-altitude flights. The visual servoing controller is based on extracting features using RGB-D images and employing a Kalman filter to estimate the edges of the PV arrays. Furthermore, this work demonstrates the proposal in both simulated and real-world environments using the commercial aerial vehicle (DJI Matrice 100), with the purpose of showcasing the results of the architecture

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Data-Driven Model Predictive Control for Trajectory Tracking in UAV-Manipulator Systems

2025 , Bryan S. Guevara , Varela Aldas, José , Viviana Moya , Manuel Cardona , Daniel C. Gandolfo , Juan M. Toibero

This work presents the design and implementation of a data-driven Nonlinear Model Predictive Control (NMPC) framework for an Uncrewed Aerial Vehicle (UAV) equipped with a 3-DOF robotic arm. Real-world data was collected using the Matrice 100 platform and Dynamixel MX-28AR actuators to identify a high-dimensional linear model via Dynamic Mode Decomposition with Control (DMDc), capturing the interactions between the aerial vehicle and the manipulator across 21 state variables. This DMDc-based model is embedded within the NMPC formulation to predict system behavior over finite horizons. The UAV’s orientation is represented using quaternions, enabling continuous and singularity-free attitude control. Additionally, the redundancy of the UAV-manipulator system allows for the integration of secondary objectives into the cost function, supporting flexible task execution. To meet real-time requirements, the control problem is solved using the Acados solver. The resulting controller achieves high-precision tracking while managing internal constraints, demonstrating the potential of data-driven NMPC in aerial manipulation tasks

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Modeling of Bayesian machine learning with sparrow search algorithm for cyberattack detection in IIoT environment

2024 , Faten Khalid Karim , Varela Aldas, José , Mohamad Khairi Ishak , Ayman Aljarbouh , Samih M. Mostafa

With the fast-growing interconnection of smart technologies, the Industrial Internet of Things (IIoT) has revolutionized how industries work by connecting devices and sensors and automating regular operations via the Internet of Things (IoTs). IoT devices provide seamless diversity and connectivity in different application domains. This system and its transmission channels are subjected to targeted cyberattacks due to their round-the-clock connectivity. Accordingly, a multilevel security solution is needed to safeguard the industrial system. By analyzing the data packet, the Intrusion Detection System (IDS) counteracts the cyberattack for the targeted attack in the IIoT platform. Various research has been undertaken to address the concerns of cyberattacks on IIoT networks using machine learning (ML) and deep learning (DL) approaches. This study introduces a new Bayesian Machine Learning with the Sparrow Search Algorithm for Cyberattack Detection (BMLSSA-CAD) technique in the IIoT networks. The proposed BMLSSA-CAD technique aims to enhance security in IIoT networks by detecting cyberattacks. In the BMLSSA-CAD technique, the min-max scaler normalizes the input dataset. Additionally, the method utilizes the Chameleon Optimization Algorithm (COA)-based feature selection (FS) approach to identify the optimal feature set. The BMLSSA-CAD technique uses the Bayesian Belief Network (BBN) model for cyberattack detection. The hyperparameter tuning process employs the sparrow search algorithm (SSA) model to enhance the BBN model performance. The performance of the BMLSSA-CAD method is examined using UNSWNB51 and UCI SECOM datasets. The experimental validation of the BMLSSA-CAD method highlighted superior accuracy outcomes of 97.84% and 98.93% compared to recent techniques on the IIoT platform

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Evaluating a User Interface for M-Commerce Shoe Sales

2024 , Castillo Ledesma, Franklin Adrian , Varela Aldas, José , José Oleas-Orozco , Jonathan Toapanta

Interface engineering maximizes user experience by defining the form, function, and interfaces utility. Current interfaces have not optimized user-system interaction. This study aimed to develop a compatible and user-friendly interface to improve user efficiency. Android Studio was used to create a simple and functional interface. The commands combination was designed to prioritize simplicity, reducing user stress. Continuous evaluations support to identify areas for improvement and optimize the interface's performance. The results exposed the application developed was well-received, providing a simple and effective interface. Users reported better interaction and superior satisfaction with the system, confirming the proposed design effectiveness. The user interface developed, currently maximizes the experience, with a user rating of 75% indicating that design is simple and functional. Continuous evaluation and improvement guarantee that application will evolve to fulfill more complex future needs, contributing to user well-being and efficiency.

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