Author name cluster

Cristina Seceleanu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

2 author rows

FM Conference 2026 Conference Paper

Efficient Multi-level Mine Dewatering Using Uppaal Stratego

Muhammad Naeem
Cristina Seceleanu
Alf J. Isaksson
Tiberiu J. Seceleanu

Abstract Effective water management in underground mining requires maintaining safe reservoir levels while minimizing the high energy costs of continuous pumping. Although flexible electricity pricing enables cost-aware operation, traditional threshold-based controllers cannot exploit this flexibility efficiently. This paper presents an industrial case study on efficient mine dewatering using reinforcement-learning-based control synthesized with the Uppaal Stratego framework. A baseline threshold controller is first implemented, followed by a reinforcement-learning controller trained on forecast inflows and day-ahead electricity prices to minimize pumping costs while limiting pump switching. To ensure safety during learning without distorting the optimization objective, we introduce a pre-shield that blocks unsafe transitions. We formally show that this pre-shield is maximally permissive with respect to a monotonicity safety objective. Simulation results demonstrate that the learning-based strategy reduces total energy consumption by up to 40% compared to threshold-based control, while maintaining safe operation in all scenarios.

DOI

FormaliSE Conference 2018 Conference Paper

Formal verification of an autonomous wheel loader by model checking

Rong Gu 0002
Raluca Marinescu
Cristina Seceleanu
Kristina Lundqvist

In an attempt to increase productivity and the workers' safety, the construction industry is moving towards autonomous construction sites, where various construction machines operate without human intervention. In order to perform their tasks autonomously, the machines are equipped with different features, such as position localization, human and obstacle detection, collision avoidance, etc. Such systems are safety critical, and should operate autonomously with very high dependability (e. g. , by meeting task deadlines, avoiding (fatal) accidents at all costs, etc.). An Autonomous Wheel Loader is a machine that transports materials within the construction site without a human in the cab. To check the dependability of the loader, in this paper we provide a timed automata description of the vehicle's control system, including the abstracted path planning and collision avoidance algorithms used to navigate the loader, and we model check the encoding in UPPAAL, against various functional, timing and safety requirements. The complex nature of the navigation algorithms makes the loader's abstract modeling and the verification very challenging. Our work shows that exhaustive verification techniques can be applied early in the development of autonomous systems, to enable finding potential design errors that would incur increased costs if discovered later.

Details