arXiv subjects

Yulin Feng

Publications and source records attributed to Yulin Feng.

4 recordsLinked to original sources

arXiv2026

Sinkhorn Distributionally Robust State Estimation via System Level Synthesis

In state estimation tasks, the usual assumption of exactly known disturbance distribution is often unrealistic and renders the estimator fragile in practice. The recently emerging Wasserstein distributionally robust state estimation (DRSE) design can partially mitigate this fragility; however, its worst-case distribution is provably discrete, which deviates from the inherent continuity of real-world distributions and results in over-pessimism. In this work, we develop a new Sinkhorn DRSE design within system level synthesis scheme with the aim of shaping the closed-loop errors under the unknown continuous disturbance distribution. For uncertainty description, we adopt the Sinkhorn ambiguity set that includes an entropic regularizer to penalize non-smooth and discrete distributions within a Wasserstein ball. We present the first result of finite-sample probabilistic guarantee of the Sinkhorn ambiguity set. Then we analyze the limiting properties of our Sinkhorn DRSE design, thereby highlighting its close connection with the generic $\mathcal{H}_2$ design and Wasserstein DRSE. To tackle the min-max optimization problem, we reformulate it as a finite-dimensional convex program through duality theory. By identifying a compact subset of the feasible set guaranteed to enclose the global optimum, we develop a tailored Frank-Wolfe solution algorithm and formally establish its convergence rate. The advantage of Sinkhorn DRSE over existing design schemes is verified through numerical case studies.

math.OC↗

arXiv2025

False Data-Injection Attack Detection in Cyber-Physical Systems: A Wasserstein Distributionally Robust Reachability Optimization Approach

Cyber-physical system (CPS) is the foundational backbone of modern critical infrastructures, so ensuring its security and resilience against cyber-attacks is of pivotal importance. This paper addresses the challenge of designing anomaly detectors for CPS under false-data injection (FDI) attacks and stochastic disturbances governed by unknown probability distribution. By using the Wasserstein ambiguity set, a prevalent data-driven tool in distributionally robust optimization (DRO), we first propose a new security metric to deal with the absence of disturbance distribution. This metric is designed by asymptotic reachability analysis of state deviations caused by stealthy FDI attacks and disturbance in a distributionally robust confidence set. We then formulate the detector design as a DRO problem that optimizes this security metric while controlling the false alarm rate robustly under a set of distributions. This yields a trade-off between robustness to disturbance and performance degradation under stealthy attacks. The resulting design problem turns out to be a challenging semi-infinite program due to the existence of distributionally robust chance constraints. We derive its exact albeit non-convex reformulation and develop an effective solution algorithm based on sequential minimization. Finally, a case study on a simulated three-tank is illustrated to demonstrate the efficiency of our design in robustifying against unknown disturbance distribution.

math.DS↗

arXiv2024

Distributionally Robust Fault Detection Trade-off Design with Prior Fault Information

The robustness of fault detection algorithms against uncertainty is crucial in the real-world industrial environment. Recently, a new probabilistic design scheme called distributionally robust fault detection (DRFD) has emerged and received immense interest. Despite its robustness against unknown distributions in practice, current DRFD focuses on the overall detectability of all possible faults rather than the detectability of critical faults that are a priori known. Henceforth, a new DRFD trade-off design scheme is put forward in this work by utilizing prior fault information. The key contribution includes a novel distributional robustness metric of detecting a known fault and a new relaxed distributionally robust chance constraint that ensures robust detectability. Then, a new DRFD design problem of fault detection under unknown probability distributions is proposed, and this offers a flexible balance between the robustness of detecting known critical faults and the overall detectability against all possible faults. To address the resulting semi-infinite chance-constrained problem, we first reformulate it to a finite-dimensional problem characterized by bilinear matrix inequalities. Subsequently, a tailored heuristic solution algorithm is developed, which includes a sequential minimization procedure and an initialization strategy. Finally, case studies on a simulated three-tank system and a real-world battery cell are carried out to showcase the effectiveness of the proposed heuristic algorithm and the advantages of our DRFD method.

math.OC↗

arXiv2023

Layer-number-dependent spin Hall effects in transition metal monocarbides $M_{2}\rm{C}$ ($M=\rm{V}, \rm{Nb}, \rm{Ta}$)

The recent discovery of strong spin Hall effects (SHE) in 2D layered topological semimetals has attracted intensive attention due to its exotic electronic properties and potential applications in spintronic devices. In this paper, we systematically study the topological properties and intrinsic SHE of layered transition metal carbides $M_{2}\rm{C}$ ($M=\rm{V}, \rm{Nb}, \rm{Ta}$). The results show that both bulk and monolayer $M_{2}\rm{C}$ have symmetry-protected nodal points (NPs) and lines (NLs) originating from the $d$ band crossing near the Fermi level ($E_F$). The inclusion of SOC breaks the degeneracy of NLs and NPs, contributing to large spin Hall conductivity (SHC) up to $\sim$1100 and $\sim$200 $(\hbar / e)(\Omega \mathrm{cm})^{-1}$ for bulk and monolayer Ta$_{2}$C, respectively. Remarkably, we find that magnitude of SHC exhibits a significant enhancement by increasing the layer number. For eight-layer Ta$_{2}$C, the maximum value of SHC can reach up to $\sim$600 $(\hbar / e)(\Omega \mathrm{cm})^{-1}$, comparable to many reported 3D topological materials. Analysis of spin Berry curvature reveals that the large SHC originates from layer-number-dependent nodal line structure near the $E_F$, in which the repeated crossover between valence and conduction bands creates large amounts of NPs along the $\Gamma\rm{-K}$ route. Our findings not only provide a new platform for experimental research of low-dimensional SHE, but also suggest an effective way of realizing giant SHE by controlling layer thickness.

cond-mat.mtrl-sci↗