Search Results for author: Daniel W. C. Ho

Found 7 papers, 0 papers with code

Real-time Estimation of DoS Duration and Frequency for Security Control

no code implementations10 Dec 2023 Yifan Sun, Jianquan Lu, Daniel W. C. Ho, Lulu Li

In this paper, we develop a new denial-of-service (DoS) estimator, enabling defenders to identify duration and frequency parameters of any DoS attacker, except for three edge cases, exclusively using real-time data.

Synchronization of multiple rigid body systems: a survey

no code implementations4 Jun 2023 X. Jin, Daniel W. C. Ho, Y. Tang

The multi-agent system has been a hot topic in the past few decades owing to its lower cost, higher robustness, and higher flexibility.

Secure Fusion Estimation Against FDI Sensor Attacks in Cyber-Physical Systems

no code implementations30 Dec 2022 Bo Chen, Pindi Weng, Daniel W. C. Ho, Li Yu

This paper is concerned with the problem of secure multi-sensors fusion estimation for cyber-physical systems, where sensor measurements may be tampered with by false data injection (FDI) attacks.

Distributed Estimation for Interconnected Systems with Arbitrary Coupling Structures

no code implementations1 Jun 2022 Yuchen Zhang, Bo Chen, Li Yu, Daniel W. C. Ho

By merging these subsystem-level stability conditions and the optimization-based estimator gain design, the distributed, stable and optimal estimators are proposed.

Polynomial-Time Algorithms for Structurally Observable Graphs by Controlling Minimal Vertices

no code implementations29 Jun 2021 Shiyong Zhu, Jianquan Lu, Daniel W. C. Ho, Jinde Cao

Further, two minimum realization strategies are considered to induce an SOG from an arbitrarily given digraph by marking and controlling the minimal vertices, respectively.

Robust Kernel-based Distribution Regression

no code implementations21 Apr 2021 Zhan Yu, Daniel W. C. Ho, Ding-Xuan Zhou

Regularization schemes for regression have been widely studied in learning theory and inverse problems.

Learning Theory regression

Estimates on Learning Rates for Multi-Penalty Distribution Regression

no code implementations16 Jun 2020 Zhan Yu, Daniel W. C. Ho

The main contribution of the paper is to present a novel multi-penalty regularization algorithm to capture more features of distribution regression and derive optimal learning rates for the algorithm.

Learning Theory regression

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