1 code implementation • 25 Apr 2024 • Yihan Zhou, Yiwen Lu, Zishuo Li, Jiaqi Yan, Yilin Mo
However, the size of the optimization problem in DeePC grows linearly with respect to the data size, which prohibits its application due to high computational costs.
no code implementations • 18 Apr 2024 • Jiaqi Yan, Ankush Chakrabarty, Alisa Rupenyan, John Lygeros
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.
no code implementations • 18 Mar 2024 • Jiaqi Yan, Hideaki Ishii
Specifically, the normal oscillators can either detect the presence of malicious nodes or synchronize in both phases and frequencies.
no code implementations • 26 Jan 2024 • Qianhui Liu, Jiaqi Yan, Malu Zhang, Gang Pan, Haizhou Li
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.
no code implementations • 7 Nov 2023 • Jiaqi Yan, Hideaki Ishii
In this paper, we consider the problem of distributed parameter estimation in sensor networks.
no code implementations • 3 Jun 2023 • Jiaqi Yan, Kuo Li, Hideaki Ishii
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.
no code implementations • 21 Mar 2023 • Jiaqi Yan, Hideaki Ishii
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.
no code implementations • 13 May 2022 • Jiaqi Yan, Yilin Mo, Hideaki Ishii
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.
no code implementations • 7 Apr 2022 • Jiaqi Yan, Yilin Mo, Hideaki Ishii
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.
no code implementations • 31 Oct 2021 • Kuo Li, Qing-Shan Jia, Jiaqi Yan
We formulate the sampling process as a policy searching problem and give a solution from the perspective of Reinforcement Learning (RL).
no code implementations • 24 Mar 2021 • Josefine Graebener, Tung Phan-Minh, Jiaqi Yan, Qiming Zhao, Richard M. Murray
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.
no code implementations • 26 Jan 2021 • Jiaqi Yan, Xu Yang, Yilin Mo, Keyou You
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.
no code implementations • 3 Jan 2020 • Jiaqi Yan, Xiuxian Li, Yilin Mo, Changyun Wen
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.
no code implementations • 17 Jan 2018 • Shrainik Jain, Bill Howe, Jiaqi Yan, Thierry Cruanes
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.