Publications

See my Google Scholar for the updated list

Journal Transactions

  1. Changyue Song, Kaibo Liu, and Xi Zhang (2021), “Collusion Detection and Ground Truth Inference in Crowdsourcing for Labeling Tasks,” Journal of Machine Learning Research, 22(190), 1-45. (This paper received the best paper award in the Best Paper Competition in the QSR section of INFORMS, 2019) [paper] [code]
  2. Changyue Song, Ziqian Zheng, and Kaibo Liu (2021), “Building Local Models for Flexible Degradation Modeling and Prognostics,” IEEE Transactions on Automation Science and Engineering, in press. [paper] [data] [code]
  3. Minhee Kim, Changyue Song, and Kaibo Liu (2021), “Individualized Degradation Modeling and Prognostics in a Heterogeneous Group via Incorporating Static Covariate Information,” IEEE Transactions on Automation Science and Engineering, in press.
  4. Changyue Song, Kaibo Liu, and Xi Zhang (2019), “A Generic Framework for Multisensor Degradation Modeling based on Supervised Classification and Failure Surface,” IISE Transactions, 51(11), 1288-1302. (This paper was selected for the Natrella invited session in the 2019 Quality & Productivity Research Conference in Washington, DC; and selected as feature article in ISE Magazine) [paper] [data] [code]
  5. Minhee Kim, Changyue Song, and Kaibo Liu (2019), “A Generic Health Index Approach for Multisensor Degradation Modeling and Sensor Selection,” IEEE Transactions on Automation Science and Engineering, 16(3), 1426-1437. (This paper was selected for presentation in the T-ASE invited session in the 2019 INFORMS conference) [paper]
  6. Changyue Song and Kaibo Liu (2018), “Statistical Degradation Modeling and Prognostics of Multiple Sensor Signals via Data Fusion: A Composite Health Index Approach,” IISE Transactions, 50(10), 853-867. (This paper received the Best Paper Finalist award (theoretical track) in the Data Mining Section of INFORMS, 2017) [paper] [data] [code]
  7. Abdallah Chehade, Changyue Song, Kaibo Liu, Abhinav Saxena, and Xi Zhang (2018), “A Data-level Fusion Approach for Degradation Modeling and Prognostic Analysis under Multiple Failure Modes,” Journal of Quality Technology, 50(2), 150-165. (This paper received the Best Student Paper Finalist award (2nd place) in the QCRE Section of IISE Annual Conference 2016).
  8. Changyue Song, Kaibo Liu, and Xi Zhang (2017), “Integration of Data-level Fusion Model and Kernel Methods for Degradation Modeling and Prognostic Analysis,” IEEE Transactions on Reliability, 67(2), 640-650. [paper] [data] [code]
  9. Kaibo Liu, Abdallah Chehade, and Changyue Song (2017), “Optimize the Signal Quality of the Composite Health Index via Data Fusion for Degradation Modeling and Prognostic Analysis," IEEE Transactions on Automation Science and Engineering, 14(3), 1504-1514. (This paper received the Best Student Poster award in Quality, Statistics, and Reliability Section of INFORMS, 2015)
  10. Changyue Song, Kaibo Liu, Xi Zhang, Lili Chen, and Xiaochen Xian (2016), "An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model from ECG Signals," IEEE Transactions on Biomedical Engineering, 63(7), 1532-1542. (This paper was selected as the Best Student Paper Finalist in the Industrial and Systems Engineering Research Conference (ISERC), 2015) [paper]
  11. Lili Chen, Xi Zhang, and Changyue Song (2015), "An Automatic Screening Approach for Obstructive Sleep Apnea Diagnosis Based on Single-Lead Electrocardiogram," IEEE Transactions on Automation Science and Engineering, 12(1), 106-115.

Conference Papers

  1. Lili Chen, Xi Zhang, and Changyue Song (2013), "A severity measurement system for obstructive sleep apnea discrimination using a single ECG signal," in Automation Science and Engineering (CASE), 2013 IEEE International Conference on, IEEE, 2013 (This paper receives the Best Paper Award).

Book Chapters

  1. Lili Chen, Changyue Song, and Xi Zhang (2016), “Statistical Modeling of Electrocardiography Signal for Subject Monitoring and Diagnosis,” Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, John Wiley & Sons.