335 research outputs found
hybrid automata and e analysis on a neural oscillator
In this paper we propose a hybrid model of a neural oscillator, obtained by partially discretizing a well-known continuous model. Our construction points out that in this case the standard techniques, based on replacing sigmoids with step functions, is not satisfactory. Then, we study the hybrid model through both symbolic methods and approximation techniques. This last analysis, in particular, allows us to show the differences between the considered approximation approaches. Finally, we focus on approximations via e-semantics, proving how these can be computed in practice
Optical computing by injection-locked lasers
A programmable optical computer has remained an elusive concept. To construct
a practical computing primitive equivalent to an electronic Boolean logic, one
should find a nonlinear phenomenon that overcomes weaknesses present in many
optical processing schemes. Ideally, the nonlinearity should provide a
functionally complete set of logic operations, enable ultrafast all-optical
programmability, and allow cascaded operations without a change in the
operating wavelength or in the signal encoding format. Here we demonstrate a
programmable logic gate using an injection-locked Vertical-Cavity
Surface-Emitting Laser (VCSEL). The gate program is switched between the AND
and the OR operations at the rate of 1 GHz with Bit Error Ratio (BER) of 10e-6
without changes in the wavelength or in the signal encoding format. The scheme
is based on nonlinearity of normalization operations, which can be used to
construct any continuous complex function or operation, Boolean or otherwise.Comment: 47 pages, 7 figures in total, 2 tables. Intended for submission to
Nature Physics within the next two week
Functional Rehabilitation: Coordination of Artificial and Natural Controllers
International audienceWalking and standing abilities, though important for quality of life and participation in social and economic activities, can be adversely affected by central nervous system (CNS) disorders such as spinal cord injury, stroke or traumatic brain injury. One characteristic of motor deficiencies which affect lower extremities is their impact on both static and dynamic postural equilibrium. Depending on the impairment level, functional rehabilitation techniques may be needed for a patient to stand up and walk (Popovic and Sinkjær, 2003). Functional electrical stimulation (FES) can induce contraction of skeletal muscles by applying electrical stimuli to sensory-motor system via electrodes which can be placed on the skin (Kralj et al., 1983), or implanted (Guiraud et al., 2006). FES applications applied to lower limbs include foot drop correction, single joint control, cycling, standing up, walking... (Zhang and Zhu, 2007)..
Distributed learning automata-based scheme for classification using novel pursuit scheme
Author's accepted manuscript.Available from 03/03/2021.This is a post-peer-review, pre-copyedit version of an article published in Applied Intelligence. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10489-019-01627-w.acceptedVersio
From Small-Gain Theory to Compositional Construction of Barrier Certificates for Large-Scale Stochastic Systems
This paper is concerned with a compositional approach for the construction of
control barrier certificates for large-scale interconnected stochastic systems
while synthesizing hybrid controllers against high-level logic properties. Our
proposed methodology involves decomposition of interconnected systems into
smaller subsystems and leverages the notion of control sub-barrier certificates
of subsystems, enabling one to construct control barrier certificates of
interconnected systems by employing some -type small-gain conditions. The
main goal is to synthesize hybrid controllers enforcing complex logic
properties including the ones represented by the accepting language of
deterministic finite automata (DFA), while providing probabilistic guarantees
on the satisfaction of given specifications in bounded-time horizons. To do so,
we propose a systematic approach to first decompose high-level specifications
into simple reachability tasks by utilizing automata corresponding to the
complement of specifications. We then construct control sub-barrier
certificates and synthesize local controllers for those simpler tasks and
combine them to obtain a hybrid controller that ensures satisfaction of the
complex specification with some lower-bound on the probability of satisfaction.
To compute control sub-barrier certificates and corresponding local
controllers, we provide two systematic approaches based on sum-of-squares (SOS)
optimization program and counter-example guided inductive synthesis (CEGIS)
framework. We finally apply our proposed techniques to two physical case
studies
Deep Learning for Abstraction, Control and Monitoring of Complex Cyber-Physical Systems
Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative
to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep
learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant
amount of uncertainty.Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative
to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep
learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant
amount of uncertainty
IST Austria Thesis
Hybrid automata combine finite automata and dynamical systems, and model the interaction of digital with physical systems. Formal analysis that can guarantee the safety of all behaviors or rigorously witness failures, while unsolvable in general, has been tackled algorithmically using, e.g., abstraction, bounded model-checking, assisted theorem proving.
Nevertheless, very few methods have addressed the time-unbounded reachability analysis of hybrid automata and, for current sound and automatic tools, scalability remains critical. We develop methods for the polyhedral abstraction of hybrid automata, which construct coarse overapproximations and tightens them incrementally, in a CEGAR fashion. We use template polyhedra, i.e., polyhedra whose facets are normal to a given set of directions.
While, previously, directions were given by the user, we introduce (1) the first method
for computing template directions from spurious counterexamples, so as to generalize and
eliminate them. The method applies naturally to convex hybrid automata, i.e., hybrid
automata with (possibly non-linear) convex constraints on derivatives only, while for linear
ODE requires further abstraction. Specifically, we introduce (2) the conic abstractions,
which, partitioning the state space into appropriate (possibly non-uniform) cones, divide
curvy trajectories into relatively straight sections, suitable for polyhedral abstractions.
Finally, we introduce (3) space-time interpolation, which, combining interval arithmetic
and template refinement, computes appropriate (possibly non-uniform) time partitioning
and template directions along spurious trajectories, so as to eliminate them.
We obtain sound and automatic methods for the reachability analysis over dense
and unbounded time of convex hybrid automata and hybrid automata with linear ODE.
We build prototype tools and compare—favorably—our methods against the respective
state-of-the-art tools, on several benchmarks
Mathematical Analysis of Memristor CNN
In this chapter we present mathematical study of memristor systems. More precisely, we apply local activity theory in order to determine the edge of chaos regime in reaction-diffusion memristor cellular nanoscale networks (RD-MCNN) and in memristor hysteresis CNN (M-HCNN). First we give an overview of mathematical models of memristors, CNN and complexity. Then we consider the above mentioned two models and we develop constructive algorithm for determination of edge of chaos in them. Based on these algorithms numerical simulations are provided. Two applications of M-HCNN model in image processing are presented
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