Reconfiguring Hybrid Systems Using SAT
- URL: http://arxiv.org/abs/2105.08398v1
- Date: Tue, 18 May 2021 09:50:47 GMT
- Title: Reconfiguring Hybrid Systems Using SAT
- Authors: Kaja Balzereit and Oliver Niggemann
- Abstract summary: Reconfiguration aims at recovering a system from a fault by automatically adapting the system configuration.
This work presents a novel algorithm which solves three main challenges.
It is shown that the approach is able to reconfigure faults on simulated process engineering systems.
- Score: 5.208405959764275
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reconfiguration aims at recovering a system from a fault by automatically
adapting the system configuration, such that the system goal can be reached
again. Classical approaches typically use a set of pre-defined faults for which
corresponding recovery actions are defined manually. This is not possible for
modern hybrid systems which are characterized by frequent changes. Instead,
AI-based approaches are needed which leverage on a model of the non-faulty
system and which search for a set of reconfiguration operations which will
establish a valid behavior again.
This work presents a novel algorithm which solves three main challenges: (i)
Only a model of the non-faulty system is needed, i.e. the faulty behavior does
not need to be modeled. (ii) It discretizes and reduces the search space which
originally is too large -- mainly due to the high number of continuous system
variables and control signals. (iii) It uses a SAT solver for propositional
logic for two purposes: First, it defines the binary concept of validity.
Second, it implements the search itself -- sacrificing the optimal solution for
a quick identification of an arbitrary solution. It is shown that the approach
is able to reconfigure faults on simulated process engineering systems.
Related papers
- A Hybrid System for Systematic Generalization in Simple Arithmetic
Problems [70.91780996370326]
We propose a hybrid system capable of solving arithmetic problems that require compositional and systematic reasoning over sequences of symbols.
We show that the proposed system can accurately solve nested arithmetical expressions even when trained only on a subset including the simplest cases.
arXiv Detail & Related papers (2023-06-29T18:35:41Z) - An Optimization-based Deep Equilibrium Model for Hyperspectral Image
Deconvolution with Convergence Guarantees [71.57324258813675]
We propose a novel methodology for addressing the hyperspectral image deconvolution problem.
A new optimization problem is formulated, leveraging a learnable regularizer in the form of a neural network.
The derived iterative solver is then expressed as a fixed-point calculation problem within the Deep Equilibrium framework.
arXiv Detail & Related papers (2023-06-10T08:25:16Z) - Interactive System-wise Anomaly Detection [66.3766756452743]
Anomaly detection plays a fundamental role in various applications.
It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data.
We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
arXiv Detail & Related papers (2023-04-21T02:20:24Z) - AI-enhanced iterative solvers for accelerating the solution of large
scale parametrized linear systems of equations [0.0]
This paper exploits up-to-date ML tools and delivers customized iterative solvers of linear equation systems.
The results indicate its superiority over conventional iterative solution schemes.
arXiv Detail & Related papers (2022-07-06T09:47:14Z) - Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions.
For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions.
The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
arXiv Detail & Related papers (2022-05-13T21:09:41Z) - Symbolic Regression by Exhaustive Search: Reducing the Search Space
Using Syntactical Constraints and Efficient Semantic Structure Deduplication [2.055204980188575]
Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available.
In this chapter we introduce a deterministic symbolic regression algorithm specifically designed to address these issues.
A finite enumeration of all possible models is guaranteed by structural restrictions as well as a caching mechanism for detecting semantically equivalent solutions.
arXiv Detail & Related papers (2021-09-28T17:47:51Z) - Physics-integrated hybrid framework for model form error identification
in nonlinear dynamical systems [0.0]
For real-life nonlinear systems, the exact form of nonlinearity is often not known and the known governing equations are often based on certain assumptions and approximations.
We propose a novel gray-box modeling approach that not only identifies the model-form error but also utilizes it to improve the predictive capability of the known but approximate governing equation.
arXiv Detail & Related papers (2021-09-01T16:29:21Z) - Novel General Active Reliability Redundancy Allocation Problems and
Algorithm [1.5990720051907859]
The reliability redundancy allocation problem (RRAP) is used to maximize system reliability.
A novel RRAP, called the general RRAP (GRRAP), is proposed to extend the series-parallel structure or bridge network to a more general network structure.
To solve the proposed novel GRRAP, a new algorithm, called the BAT-SSOA3, used the simplified swarm optimization (SSO) to update solutions.
arXiv Detail & Related papers (2021-08-18T11:54:42Z) - A Novel Anomaly Detection Algorithm for Hybrid Production Systems based
on Deep Learning and Timed Automata [73.38551379469533]
DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems.
It combines deep learning and timed automata for creating behavioral model from observations.
The algorithm has been applied to few data sets including two from real systems and has shown promising results.
arXiv Detail & Related papers (2020-10-29T08:27:43Z) - Combining Deep Learning and Optimization for Security-Constrained
Optimal Power Flow [94.24763814458686]
Security-constrained optimal power flow (SCOPF) is fundamental in power systems.
Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs.
This paper proposes a novel approach that combines deep learning and robust optimization techniques.
arXiv Detail & Related papers (2020-07-14T12:38:21Z) - Simplified Swarm Optimization for Bi-Objection Active Reliability
Redundancy Allocation Problems [1.5990720051907859]
The reliability redundancy allocation problem (RRAP) is a well-known problem in system design, development, and management.
In this study, a bi-objective RRAP is formulated by changing the cost constraint as a new goal.
To solve the proposed problem, a new simplified swarm optimization (SSO) with a penalty function, a real one-type solution structure, a number-based self-adaptive new update mechanism, a constrained non-dominated solution selection, and a new pBest replacement policy is developed.
arXiv Detail & Related papers (2020-06-17T13:15:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.