no code implementations • 20 Aug 2023 • Hao Lu, Austin M. Bray, Chao Hu, Andrew T. Zimmerman, Hongyi Xu
During the model evaluation process, the proposed approach retrieves prediction basis samples from the health library according to the similarity of the feature importance.
no code implementations • 17 Jul 2023 • Tingkai Li, ZiHao Zhou, Adam Thelen, David Howey, Chao Hu
Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in-distribution cells with 15. 1% mean absolute percentage error using no more than the first 15% of data, for most cells.
1 code implementation • 25 Jun 2023 • Lin Wang, Xiufen Ye, Liqiang Zhu, Weijie Wu, JianGuo Zhang, Huiming Xing, Chao Hu
Notably, there is a lack of research on the application of SAM to sonar imaging.
1 code implementation • 7 May 2023 • Venkat Nemani, Luca Biggio, Xun Huan, Zhen Hu, Olga Fink, Anh Tran, Yan Wang, Xiaoge Zhang, Chao Hu
In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems.
1 code implementation • 26 Apr 2023 • Hao Lu, Adam Thelen, Olga Fink, Chao Hu, Simon Laflamme
To quantify dataset similarity between clients without explicitly sharing data, each client sets aside a local test dataset and evaluates the other clients' model prediction accuracy and uncertainty on this test dataset.
no code implementations • 24 Feb 2023 • Chao Hu, Ruishi Yu, Binqi Zeng, Yu Zhan, Ying Fu, Quan Zhang, Rongkai Liu, Heyuan Shi
Hypergraph neural networks (HGNN) have shown superior performance in various deep learning tasks, leveraging the high-order representation ability to formulate complex correlations among data by connecting two or more nodes through hyperedge modeling.
no code implementations • 25 Dec 2022 • Chao Hu, Jian Yao, Weijie Wu, Weibin Qiu, Liqiang Zhu
Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics.
no code implementations • 12 Dec 2022 • Chao Hu, Shengxin Lai
The unsupervised anomaly localization task faces the challenge of missing anomaly sample training, detecting multiple types of anomalies, and dealing with the proportion of the area of multiple anomalies.
no code implementations • 3 Dec 2022 • Chao Hu, Liqiang Zhu, Weibing Qiu, Weijie Wu
Firstly, to enhance the model's processing ability for different scale targets, a multi-scale perception module based on dilated convolution is designed, and the depth features containing multi-scale information are re-refined from both spatial and channel directions considering the inconsistency between feature maps of different scales.
no code implementations • 23 Nov 2022 • Chao Hu, Liqiang Zhu, Weibin Qiu, Weijie Wu
Recently, the vision transformer (ViT) has made breakthroughs in image recognition.
no code implementations • 18 Nov 2022 • Chao Hu, Liqiang Zhu
Aiming at the problem that the current video anomaly detection cannot fully use the temporal information and ignore the diversity of normal behavior, an anomaly detection method is proposed to integrate the spatiotemporal information of pedestrians.
no code implementations • 10 Nov 2022 • Chao Hu, Liqiang Zhu
Then, the semantic features of the motion representation are obtained through the local attention mechanism in the motion guidance module to obtain the high-level semantic features of the appearance representation.
no code implementations • 18 Oct 2022 • Chao Hu, Weibin Qiu, Weijie Wu, Liqiang Zhu
Motion features are used multiple targets in the video frame to construct spatial context simultaneously, re-encoding the target appearance and motion features, and finally reconstructing the above features through the spatio-temporal dual-stream network, and using the reconstruction error to represent the abnormal score.
no code implementations • 27 Aug 2022 • Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, Byeng D. Youn, Michael D. Todd, Sankaran Mahadevan, Chao Hu, Zhen Hu
This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions.
no code implementations • 26 Aug 2022 • Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, Byeng D. Youn, Michael D. Todd, Sankaran Mahadevan, Chao Hu, Zhen Hu
In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.
1 code implementation • 22 Nov 2021 • Ye Liu, Huifang Li, Chao Hu, Shuang Luo, Yan Luo, Chang Wen Chen
The proposed model exploits three lightweight plug-and-play modules, namely dense feature pyramid network (DenseFPN), spatial context pyramid (SCP), and hierarchical region of interest extractor (HRoIE), to aggregate global visual context at feature, spatial, and instance domains, respectively.
1 code implementation • 25 Aug 2021 • Chao Hu, Fan Wu, Weijie Wu, Weibin Qiu, Shengxin Lai
With models trained on the normal data, the reconstruction errors of anomalous scenes are usually much larger than those of normal ones.
no code implementations • 13 Jul 2019 • Lei Zhang, Weihai Chen, Chao Hu, Xingming Wu, Zhengguo Li
In this paper, a lightweight yet efficient network (S\&CNet) is proposed to obtain a good trade-off between efficiency and accuracy for the dense depth completion.