We have done solid and impact research (~60 publications)
in time series sensor data analytics in the following aspects:
1. Time
Series Representation Learning
1)
Yucheng Wang, Min Wu, Xiaoli Li, Lihua Xie, Zhenghua Chen,
"Multivariate Time Series Representation Learning via Hierarchical
Correlation Pooling Boosted Graph Neural Network", IEEE Transactions on
Artificial Intelligence 2023. [PDF].
2) Emadeldeen
Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli
Li and Cuntai Guan, "Time-Series Representation Learning via
Temporal and Contextual Contrasting", IJCAI 2021. (More than 100
citations according to Google Scholar).
3)
Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Ruqiang Yan and Xiaoli Li "Attention-Based Sequence to
Sequence Model for Machine Remaining Useful Life Prediction ",
Neurocomputing, 2021. [PDF].
4)
Zhenghua Chen, Min Wu, Rui Zhao, Feri Guretno, Ruqiang Yan and Xiaoli Li, "Machine Remaining Useful
Life Prediction via an Attention based Deep Learning Approach", IEEE
Transactions on Industrial Electronics (TIE) 2020. [PDF].(More than
100 citations according to Google Scholar).
5)
Sateesh Babu Giduthuri, Peilin Zhao and Xiaoli Li,"Deep Convolutional Neural Network Based Regression
Approach for Estimation of Remaining Useful Life", DASFAA, 2016 [PDF]. (More than 800
citations according to Google Scholar).
6)
Jian-Bo Yang, Minh Nhut Nguyen, Phyo Phyo
San, Xiaoli Li, Priyadarsini Krishnaswamy Shonali,
"Deep Convolutional Neural Networks on Multichannel Time Series for Human
Activity Recognition", IJCAI 2015, [PDF]. (More than 1300 citations according to Google Scholar)
2. Time
Series Transfer Learning (domain adaptation and generalization)
1)
Yucheng Wang, Yuecong Xu, Zhenghua Chen, Min Wu, Xiaoli Li,
"SEnsor Alignment for Multivariate Time-Series
Unsupervised Domain Adaptation", AAAI 2023. [PDF].
2)
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu,
Chee Keong Kwoh, Xiaoli Li, "Contrastive Domain Adaptation for Time-Series
via Temporal Mixup", IEEE Transactions on
Artificial Intelligence 2023. [PDF].
3)
Xiaolei Yu, Zhibin Zhao, Xingwu Zhang,
Shaohua Tian, Chee-Keong Kwoh, Xiaoli Li, Xuefeng
Chen, "A universal transfer network for machinery fault diagnosis",
Computers in Industry 2023. [PDF].
4)
Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen, Min Wu,
Chee-Keong Kwoh and Xiaoli Li "Self-supervised Autoregressive Domain
Adaptation for Time Series Data", IEEE Transactions on Neural Networks and
Learning Systems (TNNLS), 2022. [PDF].
5)
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu,
Chee-Keong Kwoh, Xiaoli Li, Cuntai Guan "ADAST: Attentive Cross-domain
EEG-based Sleep Staging Framework with Iterative Self-Training", IEEE
Transactions on Emerging Topics in Computational Intelligence, 2022. [PDF].
6)
Mohamed Ragab, Wenyu Zhang, Emadeldeen Eldele, Min Wu,
Chee-Keong Kwoh, Xiaoli Li, "Conditional Contrastive Domain Generalization
for Fault Diagnosis", IEEE Transactions on Instrumentation &
Measurement 2022. [PDF].
7) Mohamed
Ragab, Zhenghua Chen, Min Wu, Chuan-Sheng Foo, Chee-Keong Kwoh, Ruqiang Yan and Xiaoli Li, "Contrastive Adversarial
Domain Adaptation for Machine Remaining Useful Life Prediction", IEEE
Transactions on Industrial Informatics. [PDF].
8) Mohamed
Ragab, Zhenghua Chen, Min Wu, Haoliang Li, Chee-Keong Kwoh, Ruqian
Yan, Xiaoli Li, "Adversarial Multiple-Target Domain Adaptation for Fault
Classification", IEEE Transactions on Instrumentation and Measurement,
2020. [PDF].
9)
Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli
Li, "Adversarial Transfer Learning for Machine Remaining Useful Life
Prediction", IEEE International Conference on Prognostics and Health
Management (ICPHM 2020). [PDF]. (Finalist
Academic Paper Award)
10)Bo Zhang, Wei Li, Xiaoli
Li, See-Kiong Ng "Intelligent fault diagnosis under varying working
conditions based on domain adaptive convolutional neural networks", IEEE
Access 2018. [PDF].
3. Time
Series Model Compression
1)
Qing Xu, Zhenghua Chen, Keyu Wu, Chao Wang, Min Wu, and Xiaoli
Li, "KDnet-RUL: A Knowledge Distillation
Framework to Compress Deep Neural Networks for Machine Remaining Useful Life
Prediction", IEEE Transactions on Industrial Electronics, 2021. [PDF].
2)
Qing Xu, Zhenghua Chen, Mohamed Ragab, Chao Wang, Min Wu
and Xiaoli Li, "Contrastive Adversarial Knowledge Distillation for Deep
Model Compression in Time-Series Regression Tasks", Neurocomputing,
2021. [PDF].
3)
Qing Xu, Min Wu, Xiaoli Li, Kezhi Mao, Zhenghua Chen,
"Contrastive Distillation with Regularized Knowledge for Deep Model
Compression on Sensor-based Human Activity Recognition", IEEE Transactions
on Industrial Cyber-Physical Systems 2023. [PDF].
4)
Qing Xu, Zhenghua Chen, Mohamed Ragab, Chao Wang, Min Wu and
Xiaoli Li, "Contrastive Adversarial Knowledge Distillation for Deep Model
Compression in Time-Series Regression Tasks", Neurocomputing, 2021. [PDF].
4. Time
Series Benchmarking, Evaluation and Comparison
1) Emadeldeen
Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li,
"Self-supervised Learning for Label-Efficient Sleep Stage Classification:
A Comprehensive Evaluation", IEEE in Transactions on Neural Systems &
Rehabilitation Engineering 2023. [PDF].
2)
Mohamed Ragab Mohamed Adam, Emadeldeen Eldele, Wee Ling
Tan, Foo Chuan Sheng, Chen Zhenghua, Wu Min, Kwoh Chee-Keong, Xiaoli Li, "
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data",
ACM Transactions on Knowledge Discovery from Data (TKDD), 2023. [PDF].
3)
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu,
Chee Keong Kwoh, Xiaoli Li, "Self-supervised Learning for Label-Efficient
Sleep Stage Classification: A Comprehensive Evaluation", IEEE in
Transactions on Neural Systems & Rehabilitation Engineering 2023. [PDF].
5.
Time Series Privacy and Security
1) Mohamed
Ragab, Emadeldeen Eldele, Min Wu, Chuan-Sheng Foo, Xiaoli Li, and Zhenghua
Chen, "Source-Free Domain Adaptation with Temporal Imputation for Time
Series Data", KDD 2023. [PDF].
2) Anushiya
Arunan, Yan Qin, Chau Yuen, and Xiaoli Li. "A Federated Learning-based
Industrial Health Prognostics for Heterogeneous Edge Devices using Matched
Feature Extraction", IEEE Transactions on Automation Science and
Engineering, 2023. [PDF].
6.
Time Series Sensor Drift Prediction
1) Yu Zhang,
Chaudhuri Tanaya, Pan Liu, Lu Wang, Min Wu, Xiaoli Li, "DPC-IINK: A
Framework for Drift Prediction and Compensation with Inter and Intra Node
Knowledge Transfer", IEEE Sensors Journal 2023. [PDF].
2)
Tanaya Chaudhuri, Min Wu, Yu Zhang, Pan Liu and Xiaoli Li,
"An Attention based deep sequential GRU model for sensor drift
compensation", IEEE Sensors Journal, 2020. [PDF].
7. Time
Series Imbalance Data Analytics
1) Zhipeng
Xie, Liyang Jiang, Tengju
Ye, Xiaoli Li, " A Synthetic Minority Oversampling Method based on
Local Densities in Low-Dimensional Space for Imbalanced Learning ", DASFAA
2015, [PDF].
2) Hong
Cao, Chunyu Bao, Xiaoli Li, Yew-Kwong Woon, "Class Augmented Active
Learning", SDM 2014, [PDF].
3) Hong
Cao, Minh Nhut Nguyen, Clifton Chun Wei Phua, Shonali Priyadarsini
Krishnaswamy, and Xiaoli Li, "An Integrated Framework for Human
Activity Classification", ACM International Conference on Ubiquitous
Computing (2012). [PDF] .
4) Hong
Cao, Xiaoli Li, Yew-Kwong Woon, See-Kiong Ng, "SPO: Structure
Preserving Oversampling for Imbalanced Time Series Classification", IEEE
International Conference on Data Mining (ICDM 2011). [PDF]
8. Time
Series Applications
1) Qing
Xu, Min Wu, Edwin Khoo, Zhenghua Chen, and Xiaoli Li,
"A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery
Remaining Useful Life", IEEE/CAA Journal of Automatica
Sinica (JAS), 2022.
2)
Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee
Keong Kwoh, Xiaoli Li and Cuntai Guan, "An Attention-based Deep Learning
Approach for Sleep Stage Classification with Single-Channel EEG", IEEE Transactions
on Neural Systems & Rehabilitation Engineering (TNSRE), 2021. [PDF]. (2023
IEEE Engineering in Medicine and Biology Prize Paper Award, cited by
221 times in the past two years.).
3)
Jin Ruibing, Zhou Duo, Wu Min, Xiaoli Li, Chen Zhenghua,
"An adaptive and dynamical neural network for machine remaining useful
life prediction", IEEE Transactions on Industrial Informatics, 2023. [PDF].
4)
Devki Nandan Jha, Zhenghua Chen, Shudong Liu, Min Wu, Jiahan Zhang, Graham Morgan, Rajiv Ranjan, and Xiaoli Li,
"A hybrid accuracy- and energy-aware human activity recognition model in
IoT environment", IEEE Transactions on Sustainable Computing, 2022. [PDF].
5)
Yan Qin, Chau Yuen, Yimin Shao, Bo Qin, and Xiaoli Li.
"Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery
Remaining Useful Life Estimation", IEEE Transactions on Cybernetics,
2022. [PDF].
6)
Ruibing Jin, Zhenghua Chen, Keyu Wu, Min Wu, Xiaoli Li, Ruqiang Yan, "Bi-LSTM based Two-Stream Network for
Machine Remaining Useful Life Prediction", IEEE Transactions on Instrumentation
and Measurement, 2022. [PDF].
7)
Ruibing Jin, Min Wu, Keyu Wu, Kaizhou
Gao, Zhenghua Chen, Xiaoli Li, "Position Encoding based Convolutional
Neural Networks for Machine Remaining Useful Life Prediction", IEEE/CAA
Journal of Automatica Sinica,
2022. [PDF].
8)
F.Md.
Meftahul, Bangjian Zhou, JiWei Yoon, KL Low, J Pan, J
Ghosh, Min Wu, Xiaoli Li, AVY Thean, J Senthilnath, "Significance of
Activation Functions in Developing an Online Classifier for Semiconductor
Defect Detection", IEEE Journal of Biomedical and Health Informatics,
2022. [PDF].
9)
Chenyang Li,
Chee Keong Kwoh, Xiaoli Li, Lingfei Mo, Ruqiang Yan, "Rotating Machinery Fault Diagnosis Based
on Multi-sensor Information Fusion Using Graph Attention Network", The
17th International Conference on Control, Automation, Robotics and Vision (ICARCV
2022). [PDF].
10) Jiyan
Wu, Min Wu, Zhenghua Chen, Xiaoli Li and Ruqiang Yan,
"Degradation-Aware Remaining Useful Life Prediction with LSTM
Autoencoder", IEEE Transactions on Instrumentation and Measurement (TIM),
2021. [PDF].
11) Jiyan
Wu, Min Wu, Zhenghua Chen, Ruqiang Yan and Xiaoli Li,
"A Joint Classification-Regression Method for Multi-Stage Remaining Useful
Life Prediction", Journal of Manufacturing Systems, 2021. [PDF].
12)Xiaoli Li,
Peilin Zhao, Min Wu, Zhenghua Chen, and Le Zhang, "Deep learning for human
activity recognition", Neurocomputing, 2021. [PDF]
13) Zhenghua
Chen, Min Wu, Wei Cui, Chengyu Liu and Xiaoli Li, "An Attention Based
CNN-LSTM Approach for Sleep-Wake Detection with Heterogeneous Sensors",
IEEE Journal of Biomedical and Health Informatics (J-BHI), 2020. [PDF].
14) Yubo
Hou, Zhenghua Chen, Min Wu, Chuan-Sheng Foo, Xiaoli Li and Raed Shubair, "Mahalanobis
Distance based Adversarial Network for Anomaly Detection", 45th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP
2020). [PDF].
15) Zhenghua
Chen, Min Wu, Kaizhou Gao, Jiyan Wu, Jie Ding, Zeng Zeng and Xiaoli Li, "A Novel Ensemble Deep Learning
Approach for Sleep-Wake Detection Using Heart Rate Variability and
Acceleration", IEEE Transactions On Emerging
Topics In Computational Intelligence, 2020. [PDF].
16) Zhenghua
Chen, Le Zhang, Min Wu, Xiaoli Li, "Deep Learning based Human Activity
Recognition for Healthcare Services", in book "Deep Learning for
Biomedical Data Analysis: Techniques, Approaches and Applications",
Springer 2020.
17) Zhenghua
Chen, Chaoyang Jiang, Shili Xiang, Jie Ding, Min Wu, and Xiaoli Li,
"Smartphone Sensor Based Human Activity Recognition Using Feature Fusion
and Maximum Full A Posteriori", IEEE Transactions
on Instrumentation and Measurement, 2019. [PDF].
18) Zhenghua
Chen, Chaoyang Jiang, Mustafa K. Masood, Yeng Chai Soh, Min Wu, Xiaoli Li,
"Deep Learning for Building Occupancy Estimation Using Environmental
Sensors", Deep Learning: Algorithms and Applications’, Springer, edited by
Witold Pedrycz, Shyi-Ming
Chen. [PDF].
19) Chen
Zhenghua, Wu Min, Wu Jiyan, Ding Jie, Zeng Zeng, Xiaoli
Li, Karl Surmacz, "A Deep Learning Approach for Sleep-Wake Detection from
HRV and Accelerometer Data", IEEE International Conference on Biomedical
and Health Informatics (BHI'19) [PDF].
20) Shili
Xiang, Dong Huang, Xiaoli Li, Yixin Cao, Lei Hou, Shuai Wang, " A
Generalized Predictive Framework for Data Driven Prognostics and Diagnostics
using Machine Logs", TENCON 2018.
21) Phyo Phyo San, Pravin Kakar, Xiaoli Li, Priyadarsini
Krishnaswamy Shonali Jian-Bo Yang, Minh Nhut Nguyen, "Deep learning for
human activity recognition", Big Data Analytics for Sensor-Network
Collected Intelligence, 2017, [PDF].
22) Sateesh
Babu Giduthuri, Xiaoli Li, and Suresh Sundaram, "Meta-cognitive
Regression Neural Network for Function Approximation: Application to Remaining
Useful Life Estimation", IJCNN, 2016 [PDF].
9. Traditional
Machine Learning and Feature Engineering
1)
Heidar Davoudi, Xiao-Li Li, Nguyen Minh Nhut
and Shonali Priyadarsini Krishnaswamy, "Activity Recognition Using a Few
Label Samples", PAKDD 2014, [PDF].
2) Minh
Nhut Nguyen, Chunyu Bao, Kar Leong Tew, Sintiani Dewi
Teddy, and Xiaoli Li, "Ensemble based Real-time Adaptive
Classification System for Intelligent Sensing Machine Diagnostics", IEEE
Transactions on Reliability, VOL. 61, NO. 2, JUNE 2012, [PDF] .
3) Minh
Nhut Nguyen, Xiaoli Li, See-Kiong Ng, "Ensemble Based Positive Unlabeled Learning for Time Series Classification",
Lecture Notes in Computer Science, 2012, Volume 7238, Database Systems for
Advanced Applications, Pages 243-257 (International Conference on Database
Systems for Advanced Applications, DASFAA 2012). [PDF]
4)
Minh Nhut Nguyen, Xiaoli Li, See-Kiong Ng,
"Positive Unlabeled Learning for Time Series
Classification", Proceedings of the 22nd International Joint Conference on
Artificial Intelligence (IJCAI 2011), Barcelona, Spain, July 16-22, 2011. [PDF] (More than
100 citations according to Google Scholar).
5)
Kar Leong Tew, Nguyen Minh Nhut, Sintiani
Dewi Teddy, Xiaoli Li, "Real-time Adaptive Classification System for
Intelligent Sensing in Manufacturing Environment (A Feasibility Study)",
IEEE International Conference on Granular Computing, Taiwan, 2011.
6)
Kar Leong Tew, Sintiani D. T.,
Watt P.Y., Xiaoli Li, "A Systematic Approach towards Manufacturing
Health Management", Proceedings of IEEE Conf Prognostics System Health
Management, 2011.
10. Awards
1) Paper An Attention-based Deep Learning Approach for
Sleep Stage Classification with Single-Channel EEG, has
been selected 2023 IEEE Engineering in Medicine and Biology Prize Paper Award.
2) Paper Adversarial
Transfer Learning for Machine Remaining Useful Life Prediction has
been selected as the Finalist Academic Paper Award of The IEEE International Conference on Prognostics
and Health Management (ICPHM 2020).
3) Champions of Opportunity Activity Recognition
Challenge (Conducted by EU Consortium), Task B1: Automatic Activity Data
Segmentation and Task B2: Multimodal activity recognition: Gestures, 2011.
4) Paper Adversarial
Transfer Learning for Machine Remaining Useful Life Prediction" has
been selected as the Finalist Academic Paper Award of IEEE
International Conference on Prognostics and Health Management (ICPHM
2020).