5 citations · 5 across the 6 of their papers we have counts for
7 papers
Ill-Conditioned Power Flow Analysis Using a Quantized State-Based Approach
Liya Huang, Federico Milano, Georgios Tzounas
This paper focuses on power flow analysis through the lens of the Newton flow, a continuous-time formulation of Newton's method. Within this framework, we explore how quantized-sta…
TFGA-Net: Temporal-Frequency Graph Attention Network for Brain-Controlled Speaker Extraction
Youhao Si, Yuan Liao, Qiushi Han +3
The rapid development of auditory attention decoding (AAD) based on electroencephalography (EEG) signals offers the possibility EEG-driven target speaker extraction. However, how t…
On the Duality Between Quantized Time and States in Dynamic Simulation
Liya Huang, Georgios Tzounas
This letter introduces a formal duality between discrete-time and quantized-state numerical methods. We interpret quantized state system (QSS) methods as integration schemes applie…
Brainprint-Modulated Target Speaker Extraction
Qiushi Han, Yuan Liao, Youhao Si +1
Achieving robust and personalized performance in neuro-steered Target Speaker Extraction (TSE) remains a significant challenge for next-generation hearing aids. This is primarily d…
Frequency-Based Alignment of EEG and Audio Signals Using Contrastive Learning and SincNet for Auditory Attention Detection
Yuan Liao, Yuhong Zhang, Qiushi Han +5
Humans exhibit a remarkable ability to focus auditory attention in complex acoustic environments, such as cocktail parties. Auditory attention detection (AAD) aims to identify the…
Matrix Pencil-Based Analysis of Multirate Simulation Schemes
Liya Huang, Georgios Tzounas
This paper focuses on multirate time-domain simulations of power system models. It proposes a matrix pencil-based approach to evaluate the spurious numerical deformation introduced…
CLDTA: Contrastive Learning based on Diagonal Transformer Autoencoder for Cross-Dataset EEG Emotion Recognition
Yuan Liao, Yuhong Zhang, Shenghuan Wang +5
Recent advances in non-invasive EEG technology have broadened its application in emotion recognition, yielding a multitude of related datasets. Yet, deep learning models struggle t…