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2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Carrasco-González, David Horrillo-Quintero, Pablo García-Triviño, Pablo Sarrias-Mena, Raúl García-Vázquez, Carlos Andrés Fernández-Ramirez, Luis M. University of Cadiz SURET Research Group Department of Electrical Engineering Algeciras Spain University of Cadiz SURET Research Group Department of Engineering in Automation Electronics and Computer Architecture and Networks Algeciras Spain

ISBN: (纸本)9798350347432

Nowadays, the use of quasi-Z-source cascaded H-bridge multilevel inverters (qZS-CHBMLI) in photovoltaic (PV) has gained their attention. They permit the single-stage power transformation of a PV generator adjusting the DC voltage independently in each module to operate each PV generator at maximum power. This paper develops a new control strategy that allows the system to continue operating after a fault in a module, by increasing the DC voltage in the operating modules. The control strategy is employed to a grid-connected qZS-CHBMLI, which consists of three cascaded qZSI powered by independent 4.8 kW PV cells. Under several operating circumstances, the proposed control system is evaluated. The results demonstrate how the control strategy applied to the system successfully reacts to the failure of a module. © 2023 IEEE.

关键词： Control systems

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DEVS-Based Modeling and Simulation of Data-Driven Exploration Algorithms of Lentic Water Bodies with AN ASV

DEVS-Based Modeling and Simulation of Data-Driven Exploratio...

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Annual Modeling and Simulation Conference (ANNSIM)

作者： Samuel Ferrero-Losada Eva Besada-Portas José L. Risco-Martín José A. López-Orozco Department of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain

This paper presents a Discrete Event System Specification (DEVS) modular architecture for modeling and simulating the behavior of an Autonomous Surface Vehicle (ASV), guided by gradient-free optimization and contour detection algorithms, while searching the extreme points and constant curves of a variable of interest in a water body. The ASV model integrates its dynamic differential equations and is controlled by a Guidance, Navigation, and Control (GNC) system implemented through several atomic models to facilitate changes in the behavior of any of them and test different types of regulators and guidance approaches in the future. The paper results show that the system is working correctly for the already supported missions (seeking extreme points and contour curves) and that adapting the specification to each of them is possible by modifying one of the models of the GNC.

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Simulation, Optimization and Control of Trajectories of ASVs Performing Hacbs Monitoring Missions in Lentic Waters

Simulation, Optimization and Control of Trajectories of ASVs...

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Simulation Winter Conference

作者： Alfredo González-Calvin Lía García-Perez José L. Risco-Martín Eva Besada-Portas Department of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain

Harmful Algaeand Cyanobacteria Blooms (HACBs) are dangerous dynamic processes for the users/inhabitants of the hydric resources. Their development and contingency plans can be anticipated by using Autonomous Surface Vehicles (ASVs) equipped with a self-driven system capable of deciding how to displace the ASV and its multi-parametric probe to take measurements in the 3D locations of the water body where the HACB is likely to occur. This paper presents a new self-driven system for that purpose, consistent on 1) an offline trajectory planner for the ASV that exploits the information provided by a commercial HACBs simulator to optimize, in turn, the ASV horizontal and probe vertical displacements; and 2) a guidance and control system specially designed for making the ASV follow the planned trajectories. The paper also presents a comprehensive set of simulations to evaluate our proposal’s performance and adjust its parameters.

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Predictive Modeling and Simulation System for the Management of Harmful Cyanobacteria Blooms

Predictive Modeling and Simulation System for the Management...

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Annual Modeling and Simulation Conference (ANNSIM)

作者： Beatriz Herguedas-Pinedo José L. Risco-Martín Segundo Esteban José A. López-Orozco Eva Besada-Portas Department of Computer Architecture and Automation Universidad Complutense de Madrid Madrid SPAIN

Water scarcity is increasing due to climate change, overexploitation and pollution. In addition, water bodies contain Harmful Cyanobacteria Blooms (HCBs) that produce toxins that are harmful to health, economy and environment. So far, these blooms have been assessed mainly by manual collection and analysis, or with the help of automatic instruments that acquire information from fixed locations. However, although having Early-Warning Systems (EWSs) to detect HCBs would be ideal, the procedures used do not usually provide data with sufficient resolution to anticipate their formation. Therefore, it is necessary to develop techniques and tools that combine data collection procedures with numerical simulations to detect, characterize, predict and respond to these outcrops. For this, it is proposed to implement a system for prediction and analysis of HCBs as part of an integral solution for its monitoring and management in real time, supported by a Model Based Systems Engineering (MBSE) infrastructure.

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Customization of the text-to-image diffusion model by fine-tuning for the generation of synthetic images of cyanobacterial blooms in lentic water bodies

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Expert Systems with Applications 2025年 287卷

作者： Barrientos-Espillco, Fredy Pajares, Gonzalo López-Orozco, José A. Besada-Portas, Eva Department of Computer Architecture and Automation University Complutense of Madrid Madrid28040 Spain Institute for Knowledge Technology University Complutense of Madrid Madrid28040 Spain

Cyanobacterial blooms emerge unpredictably on the surface of lentic water bodies, posing both ecological threats and public health risks. To effectively monitor these events, this study introduces the use of Machine Vision Systems (MVS) integrated into Autonomous Surface Vehicles (ASVs). These ASVs are capable of autonomous and safe navigation, enabling them to detect cyanobacterial blooms while avoiding obstacles. Convolutional Neural Networks (CNNs) are employed for early detection and continuous monitoring, but their effectiveness hinges on access to large, high-quality training datasets. Due to the sporadic and uncontrollable nature of bloom occurrences, acquiring sufficient real-world images for training and validating CNN models is a significant challenge. To overcome this, the Stable Diffusion XL (SDXL) text-to-image generative model is utilized to produce realistic synthetic images, ensuring a sufficient dataset for training. However, SDXL alone struggles to accurately depict cyanobacterial blooms. To address this limitation, DreamBooth is used to fine-tune SDXL with a small set of real bloom-specific image patches. To ensure the diversity of the synthetic dataset, detailed prompts for SDXL are generated using a Large Language Model (LLM). The combination of SDXL fine-tuning with LLM-driven prompts design applied to environmental monitoring and autonomous navigation in lentic environments represents the core innovation of this work. A dual-task CNN model is then trained on the synthetic dataset to simultaneously detect blooms and obstacles. Experimental results demonstrate the effectiveness and novelty of the proposed approach, showing improvements of up to 15.74% in object detection and 6.48% in semantic segmentation compared to the baseline dataset. © 2025 The Author(s)

关键词： Convolutional neural networks

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Deep Learning For Predicting Cerebral Metabolism Changes Along The Alzheimer’s Disease Continuum 15

Deep Learning For Predicting Cerebral Metabolism Changes Alo...

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15th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2024

作者： García-Gutiérrez, Fernando Hernández-Lorenzo, Laura Cabrera-Martín, María Nieves Matias-Guiu, Jordi A. Ayala, José L. Department of Computer Architecture and Automation Complutense University of Madrid Madrid28040 Spain Department of Nuclear Medicine Hospital Clinico San Carlos IdISSC Madrid28040 Spain Department of Neurology Hospital Clinico San Carlos IdISSC Madrid28040 Spain

ISBN: (纸本)9781950492787

In recent years, there has been a significant increase in the application of Artificial Intelligence (AI) techniques in Alzheimer’s disease (AD). However, current research primarily focuses on differentiating clinical phenotypes based on cross-sectional designs. In this study, we hypothesize that modeling additional aspects of the disease, such as variations in brain metabolism measured by [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), is possible, and can provide valuable insights into AD progression. For this purpose, we first identified the brain regions with the most pronounced brain hypometabolism in AD. Subsequently, Deep Learning (DL) models, based on feed-forward networks (FFNs) and convolutional neural networks (CNNs), were used to model variations in brain metabolism. Our findings demonstrated the feasibility of predicting trends in brain metabolism along the AD continuum. Overall, this study introduces a novel dimension to predictive modeling in AD, highlighting the relevance of predicting variations in brain metabolism. © IMCIC 2024. All rights reserved.

关键词： Neuroimaging

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Data-Driven Exploration of Lentic Water Bodies with ASVs Guided by Gradient-Free Optimization/Contour Detection Algorithms

Data-Driven Exploration of Lentic Water Bodies with ASVs Gui...

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2021 Winter Simulation Conference, WSC 2021

作者： Besada-Portas, Eva Giron-Sierra, Jose Maria Jimenez, Juan Lopez-Orozco, Jose Antonio University Complutense of Madrid Department of Computer Architecture and Automation Madrid28040 Spain

ISBN: (纸本)9781665433112

This paper presents a local-path planner for water quality monitoring involving an Autonomous Surface Vehicle (ASV). The planner determines new measuring waypoints based on the information collected so far, and on two gradient-free optimization and contour-detection algorithms. In particular, the optimization algorithm generates the locations where the variable/substance under study must be measured and use them as the waypoints of the external loop of the Guidance, Navigation and Control system of our ASV. Besides, the contour algorithm obtains useful waypoints to determine the water body locations where the variable/substance under study reaches a given value. The paper also analyzes how the approach works via progressive simulations over an ASV carefully modelled with a set of non-linear differential equations. Preliminary results suggest that the approach can be useful in real-world single-ASV water-quality monitoring missions where there is not previous knowledge of the state and location of the variable/substance under study. © 2021 IEEE.

关键词： Location

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METHODOLOGY FOR ONLINE ESTIMATION OF RHEOLOGICAL PARAMETERS IN POLYMER MELTS USING DEEP LEARNING AND MICROFLUIDICS

arXiv

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arXiv 2024年

作者： Sandubete-López, Juan Risco-Martín, José L. McMillan, Alexander H. Besada-Portas, Eva Dept. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Microfluidics Innovation Center Paris France

Microfluidic devices are increasingly used in biological and chemical experiments due to their cost-effectiveness for rheological estimation in fluids. However, these devices often face challenges in terms of accuracy, size, and cost. This study presents a methodology, integrating deep learning, modeling and simulation to enhance the design of microfluidic systems, used to develop an innovative approach for viscosity measurement of polymer melts. We use synthetic data generated from the simulations to train a deep learning model, which then identifies rheological parameters of polymer melts from pressure drop and flow rate measurements in a microfluidic circuit, enabling online estimation of fluid properties. By improving the accuracy and flexibility of microfluidic rheological estimation, our methodology accelerates the design and testing of microfluidic devices, reducing reliance on physical prototypes, and offering significant contributions to the field. © 2024, CC BY-NC-ND.

关键词： Viscosity measurement

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Methodology for Online Estimation of Rheological Parameters in Polymer Melts Using Deep Learning and Microfluidics

Methodology for Online Estimation of Rheological Parameters ...

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Simulation Winter Conference

作者： Juan Sandubete-López José L. Risco-Martín Alexander H. McMillan Eva Besada-Portas Dept. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Microfluidics Innovation Center Paris France

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

关键词： Deep learning Viscosity Accuracy Prototypes Life estimation Pressure measurement Polymers Microfluidics Testing Synthetic data

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Resilient source seeking with robot swarms

Resilient source seeking with robot swarms

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IEEE Conference on Decision and Control

作者： Antonio Acuaviva Jesus Bautista Weijia Yao Juan Jimenez Hector Garcia de Marina Department of Computer Architecture and Automation Universidad Complutense de Madrid Spain Department of Computer Engineering Automation and Robotics Research Centre for Information and Communication Technologies (CITIC-UGR) University of Granada Granada Spain School of Robotics Hunan University

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We present a solution for locating the source, or maximum, of an unknown scalar field using a swarm of mobile robots. Unlike relying on the traditional gradient information, the swarm determines an ascending direction to approach the source with arbitrary precision. The ascending direction is calculated from field strength measurements at the robot locations and their relative positions concerning the swarm centroid. Rather than focusing on individual robots, we focus the analysis on the density of robots per unit area to guarantee a more resilient swarm, i.e., the functionality remains even if individuals go missing or are misplaced during the mission. We reinforce the algorithm’s robustness by providing sufficient conditions for the swarm shape so that the ascending direction is almost parallel to the gradient. The swarm can respond to an unexpected environment by morphing its shape and exploiting the existence of multiple ascending directions. Finally, we validate our approach numerically with hundreds of robots. The fact that a large number of robots with a generic formation always calculate an ascending direction compensates for the potential loss of individuals.

关键词： Sufficient conditions Shape Focusing Position measurement Robustness Mobile robots Robots

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