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Found 15 papers, 1 papers with code
Secure Data Reconstruction: A Direct Data-Driven Approach
This paper addresses the problem of secure data reconstruction for unknown systems, where data collected from the system are susceptible to malicious manipulation.
Learning-Based Efficient Approximation of Data-Enabled Predictive Control
Specifically, the optimization problem in DeePC is decomposed into two parts: a control objective and a scoring function that evaluates the likelihood of a guessed I/O sequence, the latter of which is approximated with a size-invariant learned optimization problem.
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models
The framework consists of two phases: the (offine) meta-training phase learns a aggregated NSSM using data from source systems, and the (online) meta-inference phase quickly adapts this aggregated model to the target system using only a few data points and few online training iterations, based on local loss function gradients.
Secure Synchronization of Heterogeneous Pulse-Coupled Oscillators
Specifically, the normal oscillators can either detect the presence of malicious nodes or synchronize in both phases and frequencies.
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices.
Distributed Parameter Estimation with Gaussian Observation Noises in Time-varying Digraphs
In this paper, we consider the problem of distributed parameter estimation in sensor networks.
Resilient Distributed Parameter Estimation in Sensor Networks
In this paper, we study the problem of parameter estimation in a sensor network, where the measurements and updates of some sensors might be arbitrarily manipulated by adversaries.
Distributed Parameter Estimation under Gaussian Observation Noises
To this end, we first generalize the so-called dynamic regressor extension and mixing (DREM) algorithm to stochastic systems, with which the problem of estimating a $d$-dimensional vector parameter is transformed to that of $d$ scalar ones: one for each of the unknown parameters.
Event-Based Control for Synchronization of Stochastic Linear Systems with Application to Distributed Estimation
We propose an event-based control protocol for achieving the synchronization among agents in the mean square sense and theoretically analyze the performance of it by using a stochastic Lyapunov function, where the stability of $c$-martingales is particularly developed to handle the challenges brought by the general model of noises and the event-triggering mechanism.
A Framework for Distributed Estimation with Reduced Communication via Event-Based Strategies
This paper considers the problem of distributed estimation in a sensor network, where multiple sensors are deployed to infer the state of a linear time-invariant (LTI) Gaussian system.
An Actor-Critic Method for Simulation-Based Optimization
We formulate the sampling process as a policy searching problem and give a solution from the perspective of Reinforcement Learning (RL).
Failure-Tolerant Contract-Based Design of an Automated Valet Parking System using a Directive-Response Architecture
Increased complexity in cyber-physical systems calls for modular system design methodologies that guarantee correct and reliable behavior, both in normal operations and in the presence of failures.
A Distributed Implementation of Steady-State Kalman Filter
This paper studies the distributed state estimation in sensor network, where $m$ sensors are deployed to infer the $n$-dimensional state of a linear time-invariant (LTI) Gaussian system.
Resilient Multi-Dimensional Consensus in Adversarial Environment
To this end, this paper first considers a general class of consensus algorithms, where each benign agent computes an "auxiliary point" based on the received values and moves its state toward this point.
Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics
We find that these general approaches, when trained on a large corpus of SQL queries, provides a robust foundation for a variety of workload analysis tasks and database features, without requiring application-specific feature engineering.
