Zobrazeno 11 - 20
of 8 886
pro vyhledávání: '"Ji, Feng"'
This paper proposes a consensus-based distributed nonlinear filter with kernel mean embedding (KME). This fills with gap of posterior density approximation with KME for distributed nonlinear dynamic systems. To approximate the posterior density, the
Externí odkaz:
http://arxiv.org/abs/2312.01928
This paper proposes to analyze the motion stability of synchro-nous generator power systems using a Lagrangian model derived in the configuration space of generalized position and speed. In the first place, a Lagrangian model of synchronous generator
Externí odkaz:
http://arxiv.org/abs/2311.03737
Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, the graph may encounter uncertainties, such as having erroneous or missi
Externí odkaz:
http://arxiv.org/abs/2310.16401
In the short note, we describe a sampling construction that yields a sequence of graphons converging to a prescribed limit graphon in 1-norm. This convergence is stronger than the convergence in the cut norm, usually used to study graphon sequences.
Externí odkaz:
http://arxiv.org/abs/2310.14683
Autor:
Wang, Jimin, Zhang, Ji-Feng
Differentially private distributed stochastic optimization has become a hot topic due to the urgent need of privacy protection in distributed stochastic optimization. In this paper, two-time scale stochastic approximation-type algorithms for differen
Externí odkaz:
http://arxiv.org/abs/2310.11892
Topological Signal Processing (TSP) utilizes simplicial complexes to model structures with higher order than vertices and edges. In this paper, we study the transferability of TSP via a generalized higher-order version of graphon, known as complexon.
Externí odkaz:
http://arxiv.org/abs/2309.07169
Graphons have traditionally served as limit objects for dense graph sequences, with the cut distance serving as the metric for convergence. However, sparse graph sequences converge to the trivial graphon under the conventional definition of cut dista
Externí odkaz:
http://arxiv.org/abs/2309.05260
This paper is concerned with the optimal identification problem of dynamical systems in which only quantized output observations are available under the assumption of fixed thresholds and bounded persistent excitations. Based on a time-varying projec
Externí odkaz:
http://arxiv.org/abs/2309.04984
Due to inappropriate sample selection and limited training data, a distribution shift often exists between the training and test sets. This shift can adversely affect the test performance of Graph Neural Networks (GNNs). Existing approaches mitigate
Externí odkaz:
http://arxiv.org/abs/2308.09259
Autor:
Ji, Feng-Zhou, An, Jun-Hong
One pillar of quantum magnonics is to explore the utilization of mediation role of magnons in different platforms to develop quantum technologies. The efficient coupling between magnons and various quantum entities is a prerequisite. Here, we propose
Externí odkaz:
http://arxiv.org/abs/2308.05927