On probabilistic state space abstraction of deterministic switched systems

This paper considers the problem of controlling the complexity of the state space abstraction of a deterministic switched affine system, which must satisfy a rich specification, expressed as an Linear Temporal Logic (LTL) formula. We propose a probabilistic approach to the state space abstraction problem that enables a trade-off between complexity and accuracy of the abstraction. Instead of a deterministic finite transition system (DFTS), the state space is abstracted to a Discrete Time Markov Chain (DTMC) using a regular state space partition. The transition relations between the discrete states and the corresponding probabilities are computed based on the Chebyshev radius of the intersection between one-step reachable sets and discrete states. The resulting abstraction is complete, but not minimal, i.e., it introduces some false transitions. In order to refine the abstraction, Monte Carlo Simulation is used, which yields a confidence measure for every transition, besides the assigned probability. Given the product automaton (PA) between the DTMC and Büchi Automaton (BA) associated with the LTL formula, a (optimal) path generation algorithm and a controller synthesis algorithm complete the proposed solution. The application of the developed methodology to a benchmark case study from the literature, i.e., airplane fuel balancing, demonstrates the effectiveness of the approach.</p

On probabilistic state space abstraction of deterministic switched systems

This paper considers the problem of controlling the complexity of the state space abstraction of a deterministic switched affine system, which must satisfy a rich specification, expressed as an Linear Temporal Logic (LTL) formula. We propose a probabilistic approach to the state space abstraction problem that enables a trade-off between complexity and accuracy of the abstraction. Instead of a deterministic finite transition system (DFTS), the state space is abstracted to a Discrete Time Markov Chain (DTMC) using a regular state space partition. The transition relations between the discrete states and the corresponding probabilities are computed based on the Chebyshev radius of the intersection between one-step reachable sets and discrete states. The resulting abstraction is complete, but not minimal, i.e., it introduces some false transitions. In order to refine the abstraction, Monte Carlo Simulation is used, which yields a confidence measure for every transition, besides the assigned probability. Given the product automaton (PA) between the DTMC and Büchi Automaton (BA) associated with the LTL formula, a (optimal) path generation algorithm and a controller synthesis algorithm complete the proposed solution. The application of the developed methodology to a benchmark case study from the literature, i.e., airplane fuel balancing, demonstrates the effectiveness of the approach

Testing in Vitro of an Apifitoterapeutic Formula Against Nosema spp.

Nosema, a parasitic disease that affects adult honey bees, has a directly correlation with the losses of bee colonies, until to depopulation. The target of our study was to determine the antinosema action of an apifitoterapeutic formula that was obtained in an earlier phase of our researches. In the present study, we have had two experiences (F and N) formed by clinically healthy bees. The experimental bees have received, in vitro, naturally infested honey (7 spores by Nosema spp / field). The first experience (F, I-IX groups) was treated with apifitoterapeutic formula (10 ml/ honey kg), for 10 days (from T1 to T2 moment), while the second experience (N, with X-XVIII groups) was infested with naturally infested honey, for 20 days (from T1 to T2 moment). The first experience (F) showed 22% positive diagnosed bees, while the second experience (N) showed 89% positive diagnosed bees. In the first experience, the infestation degree was very weak (group I) and weak (group III), while the other groups were negative. The antiparasitic formula has showed, in laboratory conditions, a positive impact on experimental honey bees, with an efficiency over 78%. In the further, testing prophylactically and therapeutically will be conducted on bee families

D.3.1 Cooperation and Communication Planning Unit Concept

As Automated Vehicles (AVs) will be deployed in mixed traffic, they need to interact safely and efficiently with other traffic participants (TPs). The interACT project is working towards the safe integration of AVs into mixed traffic environments. In its Work Package (WP) 3, the interACT project aims to develop a novel Cooperation and Communication Planning Unit (CCPU) to enable the integrated planning and control of AV's behaviour, and the provision of time-synchronised Human Machine Interfaces (HMI) for both the on-board user and the other TPs. This document is the first deliverable of WP3 and presents the concept of the CCPU. Based on the interACT system architecture, each CCPU component, namely the Situation Matching, Interaction Planning, Trajectory Planning and Safety Layer, is described. Each description is followed by a detailed analysis of the current status and implementation plan. Apart from CCPU components, a detailed presentation of the Scenarios and Interactions Strategies digital catalogues (components of the Enablers functional block) is given, since they are not only part of WP3, but also essential for the realisation of the CCPU. Finally, the document focuses on the technical collaboration within WP3, to enable components development and integration. The interfaces from the interACT system architecture are further defined and documented and Robot Operating System (ROS) has been selected as the common software framework