This page has only limited features, please log in for full access.

Unclaimed
Yalan Ye
SCSE, University of Electronic Science and Technology of China, 12599 Chengdu, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 05 March 2021 in IEEE Transactions on Multimedia
Reads 0
Downloads 0

In zero-shot learning (ZSL) tasks, especially in generalized zero-shot learning (GZSL), the model tends to classify unseen test samples into seen categories, which is well known as the domain shift problem, because the model is trained from seen samples without unseen samples. Recently, generative adversarial network (GAN) based methods have achieved good performance in GZSL, which replace real unseen features by synthesizing fake ones to mitigate the domain shift. However, the domain shift problem is still not well solved, due to the lacking of unseen samples in the training progress of the GAN generator. In this paper, we propose a generative model named discriminative learning GAN (DL-GAN) to alleviate the domain shift in GZSL. Specifically, the DL-GAN is designed with three novel components: a dual-stream embedding model that aligns features to the ground-truth attributes to extract discriminative latent attributes from features, an attribute-based generative model that generates high-quality unseen features from semantic attributes to guarantee inter-class discriminability and semantic consistency, and a seen/unseen classifier that leverages validation samples to distinguish seen samples from unseen ones. Experimental results on four widely used datasets verify that our proposed approach significantly outperforms the state-of-the-art methods under the GZSL protocol.

ACS Style

Yalan Ye; Yukun He; Tongjie Pan; Jingjing Li; Heng Tao Shen. Alleviating Domain Shift via Discriminative Learning for Generalized Zero-Shot Learning. IEEE Transactions on Multimedia 2021, PP, 1 -1.

AMA Style

Yalan Ye, Yukun He, Tongjie Pan, Jingjing Li, Heng Tao Shen. Alleviating Domain Shift via Discriminative Learning for Generalized Zero-Shot Learning. IEEE Transactions on Multimedia. 2021; PP (99):1-1.

Chicago/Turabian Style

Yalan Ye; Yukun He; Tongjie Pan; Jingjing Li; Heng Tao Shen. 2021. "Alleviating Domain Shift via Discriminative Learning for Generalized Zero-Shot Learning." IEEE Transactions on Multimedia PP, no. 99: 1-1.

Journal article
Published: 10 September 2020 in Information
Reads 0
Downloads 0

In the wearable health monitoring based on compressed sensing, atrial fibrillation detection directly from the compressed ECG can effectively reduce the time cost of data processing rather than classification after reconstruction. However, the existing methods for atrial fibrillation detection from compressed ECG did not fully benefit from the existing prior information, resulting in unsatisfactory classification performance, especially in some applications that require high compression ratio (CR). In this paper, we propose a deep learning method to detect atrial fibrillation directly from compressed ECG without reconstruction. Specifically, we design a deep network model for one-dimensional ECG signals, and the measurement matrix is used to initialize the first layer of the model so that the proposed model can obtain more prior information which benefits improving the classification performance of atrial fibrillation detection from compressed ECG. The experimental results on the MIT-BIH Atrial Fibrillation Database show that when the CR is 10%, the accuracy and F1 score of the proposed method reach 97.52% and 98.02%, respectively. Compared with the atrial fibrillation detection from original ECG, the corresponding accuracy and F1 score are only reduced by 0.88% and 0.69%. Even at a high CR of 90%, the accuracy and F1 score are still only reduced by 6.77% and 5.31%, respectively. All of the experimental results demonstrate that the proposed method is superior to other existing methods for atrial fibrillation detection from compressed ECG. Therefore, the proposed method is promising for atrial fibrillation detection in wearable health monitoring based on compressed sensing.

ACS Style

Yunfei Cheng; Ying Hu; Mengshu Hou; Tongjie Pan; Wenwen He; Yalan Ye. Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix. Information 2020, 11, 436 .

AMA Style

Yunfei Cheng, Ying Hu, Mengshu Hou, Tongjie Pan, Wenwen He, Yalan Ye. Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix. Information. 2020; 11 (9):436.

Chicago/Turabian Style

Yunfei Cheng; Ying Hu; Mengshu Hou; Tongjie Pan; Wenwen He; Yalan Ye. 2020. "Atrial Fibrillation Detection Directly from Compressed ECG with the Prior of Measurement Matrix." Information 11, no. 9: 436.

Journal article
Published: 23 June 2018 in Sensors
Reads 0
Downloads 0

Wearable telemonitoring of electrocardiogram (ECG) based on wireless body Area networks (WBAN) is a promising approach in next-generation patient-centric telecardiology solutions. In order to guarantee long-term effective operation of monitoring systems, the power consumption of the sensors must be strictly limited. Compressed sensing (CS) is an effective method to alleviate this problem. However, ECG signals in WBAN are usually non-sparse, and most traditional compressed sensing recovery algorithms have difficulty recovering non-sparse signals. In this paper, we proposed a fast and robust non-sparse signal recovery algorithm for wearable ECG telemonitoring. In the proposed algorithm, the alternating direction method of multipliers (ADMM) is used to accelerate the speed of block sparse Bayesian learning (BSBL) framework. We used the famous MIT-BIH Arrhythmia Database, MIT-BIH Long-Term ECG Database and ECG datasets collected in our practical wearable ECG telemonitoring system to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm can directly recover ECG signals with a satisfactory accuracy in a time domain without a dictionary matrix. Due to acceleration by ADMM, the proposed algorithm has a fast speed, and also it is robust for different ECG datasets. These results suggest that the proposed algorithm is very promising for wearable ECG telemonitoring.

ACS Style

Yunfei Cheng; Yalan Ye; Mengshu Hou; Wenwen He; Yunxia Li; Xuesong Deng. A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning. Sensors 2018, 18, 2021 .

AMA Style

Yunfei Cheng, Yalan Ye, Mengshu Hou, Wenwen He, Yunxia Li, Xuesong Deng. A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning. Sensors. 2018; 18 (7):2021.

Chicago/Turabian Style

Yunfei Cheng; Yalan Ye; Mengshu Hou; Wenwen He; Yunxia Li; Xuesong Deng. 2018. "A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning." Sensors 18, no. 7: 2021.

Journal article
Published: 16 February 2017 in Sensors
Reads 0
Downloads 0

The estimation of heart rate (HR) based on wearable devices is of interest in fitness. Photoplethysmography (PPG) is a promising approach to estimate HR due to low cost; however, it is easily corrupted by motion artifacts (MA). In this work, a robust approach based on random forest is proposed for accurately estimating HR from the photoplethysmography signal contaminated by intense motion artifacts, consisting of two stages. Stage 1 proposes a hybrid method to effectively remove MA with a low computation complexity, where two MA removal algorithms are combined by an accurate binary decision algorithm whose aim is to decide whether or not to adopt the second MA removal algorithm. Stage 2 proposes a random forest-based spectral peak-tracking algorithm, whose aim is to locate the spectral peak corresponding to HR, formulating the problem of spectral peak tracking into a pattern classification problem. Experiments on the PPG datasets including 22 subjects used in the 2015 IEEE Signal Processing Cup showed that the proposed approach achieved the average absolute error of 1.65 beats per minute (BPM) on the 22 PPG datasets. Compared to state-of-the-art approaches, the proposed approach has better accuracy and robustness to intense motion artifacts, indicating its potential use in wearable sensors for health monitoring and fitness tracking.

ACS Style

Yalan Ye; Wenwen He; Yunfei Cheng; Wenxia Huang; Zhilin Zhang. A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts. Sensors 2017, 17, 385 .

AMA Style

Yalan Ye, Wenwen He, Yunfei Cheng, Wenxia Huang, Zhilin Zhang. A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts. Sensors. 2017; 17 (2):385.

Chicago/Turabian Style

Yalan Ye; Wenwen He; Yunfei Cheng; Wenxia Huang; Zhilin Zhang. 2017. "A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts." Sensors 17, no. 2: 385.

Journal article
Published: 06 October 2009 in Science in China Series F: Information Sciences
Reads 0
Downloads 0

In many applications, such as biomedical engineering, it is often required to extract a desired signal instead of all source signals. This can be achieved by blind source extraction (BSE) or semi-blind source extraction, which is a powerful technique emerging from the neural network field. In this paper, we propose an efficient semi-blind source extraction algorithm to extract a desired source signal as its first output signal by using a priori information about its kurtosis range. The algorithm is robust to outliers and spiky noise because of adopting a classical robust contrast function. And it is also robust to the estimation errors of the kurtosis range of the desired signal providing the estimation errors are not large. The algorithm has good extraction performance, even in some poor situations when the kurtosis values of some source signals are very close to each other. Its convergence stability and robustness are theoretically analyzed. Simulations and experiments on artificial generated data and real-world data have confirmed these results.

ACS Style

Yalan Ye; Phillip Sheu; Jiazhi Zeng; Gang Wang; Ke Lu. An efficient semi-blind source extraction algorithm and its applications to biomedical signal extraction. Science in China Series F: Information Sciences 2009, 52, 1863 -1874.

AMA Style

Yalan Ye, Phillip Sheu, Jiazhi Zeng, Gang Wang, Ke Lu. An efficient semi-blind source extraction algorithm and its applications to biomedical signal extraction. Science in China Series F: Information Sciences. 2009; 52 (10):1863-1874.

Chicago/Turabian Style

Yalan Ye; Phillip Sheu; Jiazhi Zeng; Gang Wang; Ke Lu. 2009. "An efficient semi-blind source extraction algorithm and its applications to biomedical signal extraction." Science in China Series F: Information Sciences 52, no. 10: 1863-1874.

Conference paper
Published: 01 March 2007 in 2007 IEEE International Conference on Integration Technology
Reads 0
Downloads 0

Hierarchy Mobile IPv6 (HMIPv6) is a scheme for micro mobility management to help basic Mobile IP reduce the signal cost and the packet loss during the periods of handover. In this paper, we describe and analyze the protocol based on, instead of the conventional ways of message sequence charts, but the Colored Petri Net (CPN). CPN is suitable to model a system from the dynamic perspective and has the ability to analyze it in a formal way. The CPN based modeling of HMIPv6 is built in this paper, highlighting the mechanism of packet forwarding and micro mobility management. The model is also analyzed by means of the occurrence graph (OG), through which we find an exceptional state would cause packet loss in the seamless handover algorithm used in HMIPv6.

ACS Style

Lei Peng; Lei Wu; Yalan Ye; Fengqi Yu; Hai Yuan. CPN Modeling and Analysis of HMIPv6. 2007 IEEE International Conference on Integration Technology 2007, 68 -73.

AMA Style

Lei Peng, Lei Wu, Yalan Ye, Fengqi Yu, Hai Yuan. CPN Modeling and Analysis of HMIPv6. 2007 IEEE International Conference on Integration Technology. 2007; ():68-73.

Chicago/Turabian Style

Lei Peng; Lei Wu; Yalan Ye; Fengqi Yu; Hai Yuan. 2007. "CPN Modeling and Analysis of HMIPv6." 2007 IEEE International Conference on Integration Technology , no. : 68-73.

Conference paper
Published: 01 January 2007 in 2007 International Conference on Machine Learning and Cybernetics
Reads 0
Downloads 0

This paper proposes a novel evolutionary immune network used for data clustering analysis. Its immune mechanism, partially inspired by self-organized mapping theory, is introduced to adjust the antibody's quantity and improve clustering quality. In order to guarantee clustering quality for highly non-linear distributed inputs, Kernel method is adopted to increase the clustering quality. In order to enhance direct descriptions about the clustering's center and result in input space, a new distance dimension instead of Euclidean distance is introduced by adopting Kernel substitution method while the training procedure is still running in input space. Simulation results are also provided to verify the algorithm's feasibility, clustering performance and anti-noise capability.

ACS Style

Lei Wu; Lei Peng; Ya-Lan Ye. An Evolutionary Immune Network Based on Kernel Method for Data Clustering. 2007 International Conference on Machine Learning and Cybernetics 2007, 3, 1759 -1764.

AMA Style

Lei Wu, Lei Peng, Ya-Lan Ye. An Evolutionary Immune Network Based on Kernel Method for Data Clustering. 2007 International Conference on Machine Learning and Cybernetics. 2007; 3 ():1759-1764.

Chicago/Turabian Style

Lei Wu; Lei Peng; Ya-Lan Ye. 2007. "An Evolutionary Immune Network Based on Kernel Method for Data Clustering." 2007 International Conference on Machine Learning and Cybernetics 3, no. : 1759-1764.

Conference paper
Published: 28 April 2006 in 2005 International Conference on Neural Networks and Brain
Reads 0
Downloads 0
ACS Style

Zhi-Lin Zhang; Yalan Ye. Extended Barros's extraction algorithm with its application in fetal ECG extraction. 2005 International Conference on Neural Networks and Brain 2006, 1 .

AMA Style

Zhi-Lin Zhang, Yalan Ye. Extended Barros's extraction algorithm with its application in fetal ECG extraction. 2005 International Conference on Neural Networks and Brain. 2006; ():1.

Chicago/Turabian Style

Zhi-Lin Zhang; Yalan Ye. 2006. "Extended Barros's extraction algorithm with its application in fetal ECG extraction." 2005 International Conference on Neural Networks and Brain , no. : 1.