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Xuewu Dai
Researcher at Northeastern University (China)
Publications - 126
Citations - 1562
Xuewu Dai is an academic researcher from Northeastern University (China). The author has contributed to research in topics: Wireless sensor network & Fault detection and isolation. The author has an hindex of 15, co-authored 99 publications receiving 1075 citations. Previous affiliations of Xuewu Dai include University of Manchester & University College London.
Papers
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Journal ArticleDOI
From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis
Xuewu Dai,Zhiwei Gao +1 more
TL;DR: An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are presented to reveal the future development direction in this field.
Journal ArticleDOI
Robust Event-Triggered Model Predictive Control for Multiple High-Speed Trains With Switching Topologies
TL;DR: This paper presents a robust event-triggered model predictive control (MPC) strategy for multiple high-speed trains (MHSTs) with random switching topologies, in which a novel event-Triggered strategy is introduced to determine when information exchange among neighboring trains and control update are executed.
Proceedings ArticleDOI
ChanEstNet: A Deep Learning Based Channel Estimation for High-Speed Scenarios
TL;DR: A channel estimation network based on deep learning, called ChanEstNet, that uses the convolutional neural network to extract channel response feature vectors and recurrent neural network for channel estimation and shows significant performance improvement in the high-speed mobile scenarios.
Journal ArticleDOI
Novel Parameter Identification by Using a High-Gain Observer With Application to a Gas Turbine Engine
TL;DR: A novel identification technique, that is high-gain observer-based identification approach, is proposed for systems with bounded process and measurement noises and an adaptive change detection and parameter identification algorithm is presented.
Journal ArticleDOI
Effective-SNR estimation for wireless sensor network using Kalman filter
TL;DR: An empirical study is presented showing that an improved indicator can be produced by combining Signal to Noise Ratio (SNR) and Link Quality Indicator (LQI) with minimal additional overhead and the estimation accuracy is further improved through the use of Kalman filtering techniques.