MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks D Li, D Chen, B Jin, L Shi, J Goh, SK Ng International Conference on Artificial Neural Networks, 703-716, 2019 | 1014 | 2019 |
Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series D Li, D Chen, J Goh, S Ng arXiv preprint arXiv:1809.04758, 2018 | 338 | 2018 |
A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis D Li, G Hu, CJ Spanos Energy and Buildings 128, 519-529, 2016 | 173 | 2016 |
Design Automation for Smart Building Systems R Jia, B Jin, M Jin, Y Zhou, IC Konstantakopoulos, H Zou, J Kim, D Li, ... Proceedings of the IEEE 106 (9), 1680-1699, 2018 | 109 | 2018 |
Fault detection and diagnosis for building cooling system with a tree-structured learning method D Li, Y Zhou, G Hu, CJ Spanos Energy and Buildings 127, 540-551, 2016 | 107 | 2016 |
Optimal sensor configuration and feature selection for AHU fault detection and diagnosis D Li, Y Zhou, G Hu, CJ Spanos IEEE Transactions on Industrial Informatics 13 (3), 1369-1380, 2016 | 62 | 2016 |
Handling Incomplete Sensor Measurements in Fault Detection and Diagnosis for Building HVAC Systems D Li, Y Zhou, G Hu, CJ Spanos IEEE Transactions on Automation Science and Engineering 17 (2), 833-846, 2019 | 42 | 2019 |
A one-class support vector machine calibration method for time series change point detection B Jin, Y Chen, D Li, K Poolla, A Sangiovanni-Vincentelli 2019 IEEE International Conference on Prognostics and Health Management …, 2019 | 39 | 2019 |
Detecting and diagnosing incipient building faults using uncertainty information from deep neural networks B Jin, D Li, S Srinivasan, SK Ng, K Poolla, A Sangiovanni-Vincentelli 2019 IEEE International Conference on Prognostics and Health Management …, 2019 | 37 | 2019 |
Driver attention prediction based on convolution and transformers C Gou, Y Zhou, D Li The Journal of Supercomputing 78 (6), 8268-8284, 2022 | 34 | 2022 |
Identifying Unseen Faults for Smart Buildings by Incorporating Expert Knowledge With Data D Li, Y Zhou, G Hu, CJ Spanos IEEE Transactions on Automation Science and Engineering 16 (3), 1412-1425, 2018 | 30 | 2018 |
MAD-SGCN: Multivariate Anomaly Detection with Self-learning Graph Convolutional Networks P Qi, D Li, SK Ng 2022 IEEE 38th International Conference on Data Engineering (ICDE), 1232-1244, 2022 | 16 | 2022 |
Learning optimization friendly comfort model for hvac model predictive control Y Zhou, D Li, CJ Spanos 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 430-439, 2015 | 13 | 2015 |
Fusing system configuration information for building cooling plant fault detection and severity level identification D Li, Y Zhou, G Hu, CJ Spanos 2016 IEEE International Conference on Automation Science and Engineering …, 2016 | 9 | 2016 |
Optimal training and efficient model selection for parameterized large margin learning Y Zhou, JY Baek, D Li, CJ Spanos Pacific-Asia Conference on Knowledge Discovery and Data Mining, 52-64, 2016 | 8 | 2016 |
Electrocardiogram classification and visual diagnosis of atrial fibrillation with DenseECG D Chen, D Li, X Xu, R Yang, SK Ng arXiv preprint arXiv:2101.07535, 2021 | 7* | 2021 |
Face It Yourselves: An LLM-Based Two-Stage Strategy to Localize Configuration Errors via Logs S Shan, Y Huo, Y Su, Y Li, D Li, Z Zheng arXiv preprint arXiv:2404.00640, 2024 | 4 | 2024 |
CB-GAN: Generate Sensitive Data with a Convolutional Bidirectional Generative Adversarial Networks R Hu, D Li, SK Ng, Z Zheng International Conference on Database Systems for Advanced Applications, 159-174, 2023 | 3 | 2023 |
VC-GAN: classifying vessel types by maritime trajectories using generative adversarial networks D Li, H Liu, SK Ng 2020 IEEE 32nd International Conference on Tools with Artificial …, 2020 | 3 | 2020 |
Fault detection and diagnosis for chillers and AHUs of building ACMV systems D Li PhD thesis, 2017 | 3 | 2017 |