arXiv subjects

Marco Muñiz

Publications and source records attributed to Marco Muñiz.

3 recordsLinked to original sources

arXiv2025

Data-Selective Online Battery Identification Using Extended Time Regular Expressions

In this paper, we propose a data-efficient online battery identification method which targets highly informative battery cell data segments based on the driving pattern of the vehicle. We consider the case of a vehicle driving on/off a motorway and construct an Extended Time Regular Expression (ETRE) to detect data segments fitting these driving patterns. Simulation results indicate that by only using up to 10.71% of the data on average, the proposed method provides a low-bias and low-variance estimator under non-negligible current and voltage noise compared to other conventional estimation algorithms.

eess.SY↗

arXiv2021

Stubborn Set Reduction for Two-Player Reachability Games

Partial order reductions have been successfully applied to model checking of concurrent systems and practical applications of the technique show nontrivial reduction in the size of the explored state space. We present a theory of partial order reduction based on stubborn sets in the game-theoretical setting of 2-player games with reachability objectives. Our stubborn reduction allows us to prune the interleaving behaviour of both players in the game, and we formally prove its correctness on the class of games played on general labelled transition systems. We then instantiate the framework to the class of weighted Petri net games with inhibitor arcs and provide its efficient implementation in the model checker TAPAAL. Finally, we evaluate our stubborn reduction on several case studies and demonstrate its efficiency.

cs.LO↗

arXiv2020

Near Optimal Task Graph Scheduling with Priced Timed Automata and Priced Timed Markov Decision Processes

Task graph scheduling is a relevant problem in computer science with application to diverse real world domains. Task graph scheduling suffers from a combinatorial explosion and thus finding optimal schedulers is a difficult task. In this paper we present a methodology for computing near-optimal preemptive and non-preemptive schedulers for task graphs. The task graph scheduling problem is reduced to location reachability via the fastest path in Priced Timed Automata (PTA) and Priced Timed Markov Decision Processes (PTMDP). Additionally, we explore the effect of using chains to reduce the computation time for finding schedules. We have implemented our models in UPPAAL CORA and UPPAAL STRATEGO. We conduct an exhaustive experimental evaluation where we compare our resulting schedules with the best-known schedules of a state of the art tool. A significant number of our resulting schedules are shown to be shorter than or equal to the best-known schedules.

cs.DC↗