https://hdl.handle.net/2072/452950 2026-04-15T19:36:37Z 2026-04-15T19:36:37Z Serrano, Fernando Puig Cayuela, Vicenç Munoz-Pacheco, Jesús M. https://hdl.handle.net/2117/460157 2026-04-15T10:39:18Z 2025-01-01T00:00:00Z

Stabilization of a solitary wave in a spatio temporal 1D uncertain Navier-Stokes equation by passivity based boundary control Serrano, Fernando; Puig Cayuela, Vicenç; Munoz-Pacheco, Jesús M. In this paper is presented a stabilization technique for a solitary wave found in a spatio-temporal 1D uncertain Navier-Stokes equation by passivity based control. First, a full dynamic analysis is proposed in order to take into consideration the characteristics of the solitary wave found in this Navier-Stokes equation. This analysis consists into the separation of the solitary wave found in the Navier-Stokes equation. This research study aims to stabilizes the solitary waves found in a numerical wave tank water reservoir. By this way the main objective is to protect renewable energy systems, such as offshore aeolic or wave energy converter renewable energy systems. The passivity based boundary controller designed in this research study consists into the selection of an appropriate Lyapunov functional to select the appropriate boundary control law. It is important to recall also, that the input-output characteristics of the 1D uncertain Navier-Stokes equation allows us to establish the appropriate storage function in order to focus in the stabilization of the solitary wave found in this mathematical model. Extensive simulations are performed in order to validate the theoretical results found in this research study. The discussions and conclusions of this work are presented.; Peer Reviewed; Postprint (published version)

2025-01-01T00:00:00Z Barceló Cuerda, Alex Cajas Ordoñez, Sebastián A. Samanta, Jaydeep Suárez Cetrulo, Andrés L. Ghosh, Romila Simón Carbajo, Ricardo Queralt Calafat, Anna https://hdl.handle.net/2117/460155 2026-04-15T10:38:29Z 2026-05-01T00:00:00Z

Offloading artificial intelligence workloads across the computing continuum by means of active storage systems Barceló Cuerda, Alex; Cajas Ordoñez, Sebastián A.; Samanta, Jaydeep; Suárez Cetrulo, Andrés L.; Ghosh, Romila; Simón Carbajo, Ricardo; Queralt Calafat, Anna The increasing demand for artificial intelligence (AI) workloads across diverse computing environments has driven the need for more efficient data management strategies. Traditional cloud-based architectures struggle to handle the sheer volume and velocity of AI-driven data, leading to inefficiencies in storage, computation, and data movement. This paper explores the integration of active storage systems within the computing continuum to optimize AI workload distribution. By embedding computation directly into storage architectures, active storage is able to reduce data transfer overhead, enhancing performance and improving resource utilization. Other existing frameworks and architectures offer mechanisms to distribute certain AI processes across distributed environments; however, they lack the flexibility and adaptability that the continuum requires, both regarding the heterogeneity of devices and the rapid-changing algorithms and models being used by domain experts and researchers. This article proposes a software architecture aimed at seamlessly distributing AI workloads across the computing continuum, and presents its implementation using mainstream Python libraries and dataClay, an active storage platform. The evaluation shows the benefits and trade-offs regarding memory consumption, storage requirements, training times, and execution efficiency across different devices. Experimental results demonstrate that the process of offloading workloads through active storage significantly improves memory efficiency and training speeds while maintaining accuracy. Our findings highlight the potential of active storage to revolutionize AI workload management, making distributed AI deployments more scalable and resource-efficient with a very low entry barrier for domain experts and application developers.; This work was partially supported by the projects PID2019107255GB-C21 and PID2023-147979NB-C21, funded by MCIN/AEI/ 10.13039 / 501100011033, and by FEDER, UE. This work was partially supported by the project “Towards a functional continuum operating system (ICOS)” funded by the European Commission under Project code/Grant Number 101070177 through the HORIZON EU program. This work was partially supported by the project “Open CloudEdgeIoT Platform Uptake in Large Scale Cross-Domain Pilots (O-CEI)” funded by the European Commission under Project code/Grant Number 101189589 through the HORIZON EUprogram. Anna Queralt is a Serra-Hunter Fellow and has been partially supported by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2023-152841OAI00 / AEI/10.13039/501100011033 (TALC).; Peer Reviewed; Postprint (author's final draft)

2026-05-01T00:00:00Z Hernandez Gomez, Octavio Adrian Begovich Mendoza, Ofelia Puig Cayuela, Vicenç Delgado Aguiñaga, Jorge Alejandro https://hdl.handle.net/2117/460197 2026-04-15T10:32:07Z 2025-01-01T00:00:00Z

Non-concurrent leak diagnosis in pipelines using an LPV Kalman filter approach Hernandez Gomez, Octavio Adrian; Begovich Mendoza, Ofelia; Puig Cayuela, Vicenç; Delgado Aguiñaga, Jorge Alejandro This paper addresses the non-concurrent leak diagnosis problem in pipelines through a discrete-time LPV Kalman filter, considering the availability of pressure head and flow rate measurements at the ends of the pipeline. This method avoids linearization while preserving the nonlinear dynamics of the system via an LPV-based representation and the use of the nonlinear embedding approach. Simulations involving two and three leaks demonstrate the accuracy of the method in estimating leak positions and magnitudes.; In loving memory of Dr. Begovich, who passed away on January 13, 2025. She will always be remembered with love by all her students and colleagues. The presented work has been made possible thanks to the master fellowship 1290802 provided by CONAHCYT, and IRI-UPC for their fruitful collaboration in this research.; Peer Reviewed; Postprint (published version)

2025-01-01T00:00:00Z Bessa, Iury da Costa Peixoto, Márcia Luciana Coutinho, Pedro Henrique Puig Cayuela, Vicenç Martínez Palhares, Reinaldo https://hdl.handle.net/2117/460254 2026-04-15T10:30:35Z 2025-06-09T00:00:00Z

Fault hiding of nonlinear parameter varying systems Bessa, Iury; da Costa Peixoto, Márcia Luciana; Coutinho, Pedro Henrique; Puig Cayuela, Vicenç; Martínez Palhares, Reinaldo © 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; This letter addresses the problem of fault-tolerant control of nonlinear parameter-varying (N-LPV) systems. Specifically, it focuses on N-LPV models that effectively represent nonlinear systems by partially embedding nonlinearities while preserving sector-bounded terms. This representation simplifies control design by reducing the number of polytope vertices compared to traditional linear parameter-varying approaches. The main contribution of this letter is to present novel fault-hiding approaches for fault-tolerant control of N-LPV models. The proposed approach guarantees recovery of the asymptotic stability for N-LPV systems under multiplicative sensor and actuator faults using a generic static N-LPV structure. Moreover, it guarantees the input-to-state stability of the reconfigured system under additive faults.; This work was supported in part by the National Research Agency (ANR) (AAP Chaire HUMA2IN) under Grant ANR-23-CPJ1-0116-01; in part by the National Center for Scientific Research (CNRS); in part by the Brazilian Agencies CNPq under Grant 407885/2023-4 and Grant 305578/2022-7; in part by FAPEAM under Grant 01.02.016301.00292/2025-80; and in part by CAPES-COFECUB Program under Grant 88887.987117/2024-00, Grant Ma1076/25, and Grant 52828NB.; Peer Reviewed; Postprint (author's final draft)

2025-06-09T00:00:00Z