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Yuxuan Yang - IEEE Xplore Author Profile
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Yuxuan Yang

Publications
8
Citations
776
Publications by Year
20162024
Co-Authors:
Show All Co-Authors (18)
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Yuxuan Yang

Affiliation

School of Computing and Artificial Intelligence
Southwestern University of Finance and Economics
Chengdu, China

Publication Topics

Biography

Yuxuan Yang received the bachelor’s degree in automation from Anhui University, Hefei, China, in 2014, the master’s degree in automation from the School of Electrical and Information Engineering, Tianjin University, Tianjin, China, in 2017, where she is currently pursuing the Ph.D. degree with the School of Electrical and Information Engineering.,Her current research interests include brain–computer interface, machine learning, and complex networks.(Based on document published on 10 January 2019).
Publications
8
Citations
776
Publications by Year
20162024
Co-Authors:
Show All Co-Authors (18)
Author's Published Works

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A Lightweight Model for Physiological Signals-Based Sleep Staging With Multiclass CAM for Model Explainability

Yuxuan Yang;Yanli Li

IEEE Sensors Journal
Year: 2024 | Volume: 24, Issue: 17 | Journal Article |
Physiological signals-based sleep staging is essential for diagnosing sleep disorders. Automatic classification using deep learning models could save time and labor, compared to manual classification and monitoring, which stimulated numerous studies aiming to improve performance with various deep learning models. However, most existing models have high computational requirements due to numerous tr...Show More

A Lightweight Model for Physiological Signals-Based Sleep Staging With Multiclass CAM for Model Explainability

Yuxuan Yang;Yanli Li

Year: 2024 | Volume: 24, Issue: 17 | Journal Article |

A Channel-Fused Dense Convolutional Network for EEG-Based Emotion Recognition

Zhongke Gao;Xinmin Wang;Yuxuan Yang;Yanli Li;Kai Ma;Guanrong Chen

IEEE Transactions on Cognitive and Developmental Systems
Year: 2021 | Volume: 13, Issue: 4 | Journal Article |
Cited by: Papers (106)
Human emotion recognition could greatly contribute to human–computer interaction with promising applications in artificial intelligence. One of the challenges in recognition tasks is learning effective representations with stable performances from electroencephalogram (EEG) signals. In this article, we propose a novel deep-learning framework, named channel-fused dense convolutional network, for EE...Show More

A Channel-Fused Dense Convolutional Network for EEG-Based Emotion Recognition

Zhongke Gao;Xinmin Wang;Yuxuan Yang;Yanli Li;Kai Ma;Guanrong Chen

Year: 2021 | Volume: 13, Issue: 4 | Journal Article |

A Complex Network-Based Broad Learning System for Detecting Driver Fatigue From EEG Signals

Yuxuan Yang;Zhongke Gao;Yanli Li;Qing Cai;Norbert Marwan;Jürgen Kurths

IEEE Transactions on Systems, Man, and Cybernetics: Systems
Year: 2021 | Volume: 51, Issue: 9 | Journal Article |
Cited by: Papers (71)
Driver fatigue detection is of great significance for guaranteeing traffic safety and further reducing economic as well as societal loss. In this article, a novel complex network (CN) based broad learning system (CNBLS) is proposed to realize an electroencephalogram (EEG)-based fatigue detection. First, a simulated driving experiment was conducted to obtain EEG recordings in alert and fatigue stat...Show More

A Complex Network-Based Broad Learning System for Detecting Driver Fatigue From EEG Signals

Yuxuan Yang;Zhongke Gao;Yanli Li;Qing Cai;Norbert Marwan;Jürgen Kurths

Year: 2021 | Volume: 51, Issue: 9 | Journal Article |

A Coincidence-Filtering-Based Approach for CNNs in EEG-Based Recognition

Zhongke Gao;Yanli Li;Yuxuan Yang;Na Dong;Xiong Yang;Celso Grebogi

IEEE Transactions on Industrial Informatics
Year: 2020 | Volume: 16, Issue: 11 | Journal Article |
Cited by: Papers (45)
Electroencephalogram (EEG), obtained by wearable devices, can realize effective human health monitoring. Traditional methods based on artificially designed features have achieved valid results in EEG-based recognition, and numerous studies start to apply deep learning techniques in this area. In this article, we propose a coincidence-filtering-based method to build a connection between artificial-...Show More

A Coincidence-Filtering-Based Approach for CNNs in EEG-Based Recognition

Zhongke Gao;Yanli Li;Yuxuan Yang;Na Dong;Xiong Yang;Celso Grebogi

Year: 2020 | Volume: 16, Issue: 11 | Journal Article |

EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation

Zhongke Gao;Xinmin Wang;Yuxuan Yang;Chaoxu Mu;Qing Cai;Weidong Dang;Siyang Zuo

IEEE Transactions on Neural Networks and Learning Systems
Year: 2019 | Volume: 30, Issue: 9 | Journal Article |
Cited by: Papers (337)
Driver fatigue evaluation is of great importance for traffic safety and many intricate factors would exacerbate the difficulty. In this paper, based on the spatial-temporal structure of multichannel electroencephalogram (EEG) signals, we develop a novel EEG-based spatial-temporal convolutional neural network (ESTCNN) to detect driver fatigue. First, we introduce the core block to extract temporal ...Show More

EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation

Zhongke Gao;Xinmin Wang;Yuxuan Yang;Chaoxu Mu;Qing Cai;Weidong Dang;Siyang Zuo

Year: 2019 | Volume: 30, Issue: 9 | Journal Article |

Relative Wavelet Entropy Complex Network for Improving EEG-Based Fatigue Driving Classification

Zhongke Gao;Shan Li;Qing Cai;Weidong Dang;Yuxuan Yang;Chaoxu Mu;Pan Hui

IEEE Transactions on Instrumentation and Measurement
Year: 2019 | Volume: 68, Issue: 7 | Journal Article |
Cited by: Papers (61)
Detecting fatigue driving from electroencephalogram (EEG) signals constitutes a challenging problem of continuing interest since fatigue driving has caused the majority of traffic accidents. We carry out a simulated driving experiment for EEG data acquisition. Then, we calculate the wavelet entropy under the alert and fatigue state, respectively, and find that the wavelet entropy gets an acceptabl...Show More

Relative Wavelet Entropy Complex Network for Improving EEG-Based Fatigue Driving Classification

Zhongke Gao;Shan Li;Qing Cai;Weidong Dang;Yuxuan Yang;Chaoxu Mu;Pan Hui

Year: 2019 | Volume: 68, Issue: 7 | Journal Article |

A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System

Zhongke Gao;Weidong Dang;Chaoxu Mu;Yuxuan Yang;Shan Li;Celso Grebogi

IEEE Transactions on Industrial Informatics
Year: 2018 | Volume: 14, Issue: 9 | Journal Article |
Cited by: Papers (73)
Increasingly advanced technology allows the monitoring of complex systems from a wide variety of perspectives. But the exploration of such systems from a multichannel sensor information viewpoint remains a complicated challenge of ongoing interest. In this paper, first, based on a well-designed double-layer distributed-sector conductance (DLDSC) sensor, systematic oil-water and gas-liquid two-phas...Show More

A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System

Zhongke Gao;Weidong Dang;Chaoxu Mu;Yuxuan Yang;Shan Li;Celso Grebogi

Year: 2018 | Volume: 14, Issue: 9 | Journal Article |

A Four-Sector Conductance Method for Measuring and Characterizing Low-Velocity Oil–Water Two-Phase Flows

Zhongke Gao;Yuxuan Yang;Lusheng Zhai;Ningde Jin;Guanrong Chen

IEEE Transactions on Instrumentation and Measurement
Year: 2016 | Volume: 65, Issue: 7 | Journal Article |
Cited by: Papers (83)
Measuring water holdup and characterizing the flow behavior of an oil–water two-phase flow is a contemporary and challenging problem of significant importance in industry. To address this problem, we develop a new method to design a new four-sector distributed conductance sensor. Specifically, we first use the finite-element method (FEM) to investigate the sensitivity distribution of the electric ...Show More

A Four-Sector Conductance Method for Measuring and Characterizing Low-Velocity Oil–Water Two-Phase Flows

Zhongke Gao;Yuxuan Yang;Lusheng Zhai;Ningde Jin;Guanrong Chen

Year: 2016 | Volume: 65, Issue: 7 | Journal Article |

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