Zobrazeno 1 - 10
of 250
pro vyhledávání: '"Hu Jilin"'
Publikováno v:
Ceramics-Silikáty, Vol 66, Iss 4, Pp 489-497 (2022)
SiC-ZrC composite powders were synthesized by carbothermal reduction method using starch as carbon source, zirconium dioxide as zirconium source, and silica sol as silicon source. The effects of reaction temperature on the phase composition and micro
Externí odkaz:
https://doaj.org/article/1d542fe5a4354d5b95e765849818c779
Autor:
Xu, Ronghui, Cheng, Hanyin, Guo, Chenjuan, Gao, Hongfan, Hu, Jilin, Yang, Sean Bin, Yang, Bin
Developing effective path representations has become increasingly essential across various fields within intelligent transportation. Although pre-trained path representation learning models have shown improved performance, they predominantly focus on
Externí odkaz:
http://arxiv.org/abs/2411.18428
Many methods have been proposed for unsupervised time series anomaly detection. Despite some progress, research on predicting future anomalies is still relatively scarce. Predicting anomalies is particularly challenging due to the diverse reaction ti
Externí odkaz:
http://arxiv.org/abs/2410.15997
Spatiotemporal trajectory data is vital for web-of-things services and is extensively collected and analyzed by web-based hardware and platforms. However, issues such as service interruptions and network instability often lead to sparsely recorded tr
Externí odkaz:
http://arxiv.org/abs/2410.14281
Probabilistic time series imputation has been widely applied in real-world scenarios due to its ability to estimate uncertainty of imputation results. Meanwhile, denoising diffusion probabilistic models (DDPMs) have achieved great success in probabil
Externí odkaz:
http://arxiv.org/abs/2410.13338
Autor:
Wu, Xingjian, Qiu, Xiangfei, Li, Zhengyu, Wang, Yihang, Hu, Jilin, Guo, Chenjuan, Xiong, Hui, Yang, Bin
Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning nomral patterns in the frequency domain to detect diverse abnormal subsequences, achi
Externí odkaz:
http://arxiv.org/abs/2410.12261
Autor:
Li, Zhe, Qiu, Xiangfei, Chen, Peng, Wang, Yihang, Cheng, Hanyin, Shu, Yang, Hu, Jilin, Guo, Chenjuan, Zhou, Aoying, Wen, Qingsong, Jensen, Christian S., Yang, Bin
Time Series Forecasting (TSF) is key functionality in numerous fields, including in finance, weather services, and energy management. While TSF methods are emerging these days, many of them require domain-specific data collection and model training a
Externí odkaz:
http://arxiv.org/abs/2410.11802
Autor:
Lin, Yan, Wei, Tonglong, Zhou, Zeyu, Wen, Haomin, Hu, Jilin, Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Vehicle trajectories provide valuable movement information that supports various downstream tasks and powers real-world applications. A desirable trajectory learning model should transfer between different regions and tasks without retraining, thus i
Externí odkaz:
http://arxiv.org/abs/2408.15251
Significant wave height (SWH) is a vital metric in marine science, and accurate SWH estimation is crucial for various applications, e.g., marine energy development, fishery, early warning systems for potential risks, etc. Traditional SWH estimation m
Externí odkaz:
http://arxiv.org/abs/2407.20053