21 citations · 92 across the 20 of their papers we have counts for
12 papers · 1 filter
Data-Driven Computational Methods for the Domain of Attraction and Zubov's Equation
Wei Kang, Kai Sun, Liang Xu
This paper deals with a special type of Lyapunov functions, namely the solution of Zubov's equation. Such a function can be used to characterize the domain of attraction for system…
Neural Energy Casimir Control for Port-Hamiltonian Systems
Liang Xu, Muhammad Zakwan, Giancarlo Ferrari-Trecate
The energy Casimir method is an effective controller design approach to stabilize port-Hamiltonian systems at a desired equilibrium. However, its application relies on the availabi…
DataCLUE: A Benchmark Suite for Data-centric NLP
Liang Xu, Jiacheng Liu, Xiang Pan +2
Data-centric AI has recently proven to be more effective and high-performance, while traditional model-centric AI delivers fewer and fewer benefits. It emphasizes improving the qua…
Attosecond delays of high harmonic emissions from isotopes of molecular hydrogen measured by Gouy phase XUV interferometer
Mumta Hena Mustary, Liang Xu, Wanyang Wu +6
High harmonic spectroscopy can access structural and dynamical information on molecular systems encoded in amplitude and phase of high harmonic generation (HHG) signals4. However,…
Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition
Liang Xu, Cuiling Lan, Wenjun Zeng +1
Skeleton data carries valuable motion information and is widely explored in human action recognition. However, not only the motion information but also the interaction with the env…
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design
Clara Lucía Galimberti, Luca Furieri, Liang Xu +1
Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propo…
A unified framework for Hamiltonian deep neural networks
Clara L. Galimberti, Liang Xu, Giancarlo Ferrari Trecate
Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/exploding gradients during weight optimization. To avoid this problem, we propose a class o…
Finite-sample-based Spectral Radius Estimation and Stabilizability Test for Networked Control Systems
Liang Xu, Baiwei Guo, Giancarlo Ferrari-Trecate
In the analysis and control of discrete-time linear time-invariant systems, the spectral radius of the system state matrix plays an essential role. Usually, it is assumed that syst…
A Data-Driven Convex Programming Approach to Worst-Case Robust Tracking Controller Design
Liang Xu, Mustafa Sahin Turan, Baiwei Guo +1
This paper studies finite-horizon robust tracking control for discrete-time linear systems, based on input-output data. We leverage behavioral theory to represent system trajectori…
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection
Liang Xu, Liying Zheng, Weijun Li +6
In recent studies, Lots of work has been done to solve time series anomaly detection by applying Variational Auto-Encoders (VAEs). Time series anomaly detection is a very common bu…
Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification
Shuwei Shen, Mengjuan Xu, Fan Zhang +8
Although deep convolutional neural networks (DCNNs) have achieved significant accuracy in skin lesion classification comparable or even superior to those of dermatologists, practic…
Non-conservative Design of Robust Tracking Controllers Based on Input-output Data
Liang Xu, Mustafa Sahin Turan, Baiwei Guo +1
This paper studies worst-case robust optimal tracking using noisy input-output data. We utilize behavioral system theory to represent system trajectories, while avoiding explicit s…