no code implementations • 28 Feb 2024 • Luyang Hou, Jun Yan, Yuankai Wu, Chun Wang, Tie Qiu
Energy Internet (EI) is emerging as new share economy platform for flexible local energy supplies in smart cities.
no code implementations • 27 Feb 2024 • Marsil Zakour, Partha Pratim Nath, Ludwig Lohmer, Emre Faik Gökçe, Martin Piccolrovazzi, Constantin Patsch, Yuankai Wu, Rahul Chaudhari, Eckehard Steinbach
However, existing datasets for 4D HOI (3D HOI over time) are limited to one subject inter- acting with one object only.
no code implementations • 6 Feb 2024 • Kun Wang, Hao Wu, Guibin Zhang, Junfeng Fang, Yuxuan Liang, Yuankai Wu, Roger Zimmermann, Yang Wang
In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames.
1 code implementation • 31 Dec 2023 • Wanlin Cai, Yuxuan Liang, Xianggen Liu, Jianshuai Feng, Yuankai Wu
To bridge this gap, this paper introduces MSGNet, an advanced deep learning model designed to capture the varying inter-series correlations across multiple time scales using frequency domain analysis and adaptive graph convolution.
1 code implementation • 4 Dec 2023 • Jinguo Cheng, Ke Li, Yuxuan Liang, Lijun Sun, Junchi Yan, Yuankai Wu
To address this challenge, we present the Super-Multivariate Urban Mobility Transformer (SUMformer), which utilizes a specially designed attention mechanism to calculate temporal and cross-variable correlations and reduce computational costs stemming from a large number of time series.
no code implementations • 19 Nov 2023 • Site Mo, Haoxin Wang, Bixiong Li, Songhai Fan, Yuankai Wu, Xianggen Liu
Time series is a special type of sequence data, a sequence of real-valued random variables collected at even intervals of time.
1 code implementation • 2 Aug 2023 • Chunwei Yang, Xiaoxu Chen, Lijun Sun, Hongyu Yang, Yuankai Wu
To address this gap, we propose an unsupervised method called Floss that automatically regularizes learned representations in the frequency domain.
no code implementations • 12 Dec 2022 • Qin Li, Xuan Yang, Yong Wang, Yuankai Wu, Deqiang He
This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed.
1 code implementation • 24 Nov 2022 • Lei Wang, Hongyu Yang, Yi Lin, Suwan Yin, Yuankai Wu
Although DRL has achieved important advancements in this field, the existing works pay little attention to the explainability and safety issues related to DRL controllers, particularly the safety under adversarial attacks.
1 code implementation • 14 Jul 2022 • Yuankai Wu, Hongyu Yang, Yi Lin, Hong Liu
By this means, STPN allows cross-talk of spatial and temporal factors for modeling delay propagation.
1 code implementation • 24 Sep 2021 • Yuankai Wu, Dingyi Zhuang, MengYing Lei, Aurelie Labbe, Lijun Sun
Specifically, we propose a novel spatial aggregation network (SAN) inspired by Principal Neighborhood Aggregation, which uses multiple aggregation functions to help one node gather diverse information from its neighbors.
no code implementations • 6 Sep 2021 • Yuankai Wu, Zhanhong Cheng, Lijun Sun
Individual mobility prediction is an essential task for transportation demand management and traffic system operation.
1 code implementation • 21 May 2021 • Xudong Wang, Yuankai Wu, Dingyi Zhuang, Lijun Sun
This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors.
no code implementations • 14 Mar 2021 • Manoj Rohit Vemparala, Alexander Frickenstein, Nael Fasfous, Lukas Frickenstein, Qi Zhao, Sabine Kuhn, Daniel Ehrhardt, Yuankai Wu, Christian Unger, Naveen Shankar Nagaraja, Walter Stechele
The distilled models exhibit their strength against all white box attacks with an exception of C&W.
no code implementations • 6 Jul 2020 • Tianyu Shi, Jiawei Wang, Yuankai Wu, Luis Miranda-Moreno, Lijun Sun
Instead of learning a reliable behavior for ego automated vehicle, we focus on how to improve the outcomes of the total transportation system by allowing each automated vehicle to learn cooperation with each other and regulate human-driven traffic flow.
1 code implementation • 13 Jun 2020 • Yuankai Wu, Dingyi Zhuang, Aurelie Labbe, Lijun Sun
Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis.
no code implementations • 10 May 2020 • Qin Li, Huachun Tan, Xizhu Jiang, Yuankai Wu, Linhui Ye
However, it remains a challenging task to construct an analytical framework through which the natural spatial-temporal structural properties of multivariable traffic information can be effectively represented and exploited to better understand and detect NRTC.
1 code implementation • 18 Apr 2019 • Chenyang Xi, Tianyu Shi, Yuankai Wu, Lijun Sun
Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability.
no code implementations • 25 Oct 2018 • Yuankai Wu, Huachun Tan, Bin Ran
In this paper, we propose a more effective deep reinforcement learning (DRL) model for differential variable speed limits (DVSL) control, in which the dynamic and different speed limits among lanes can be imposed.
no code implementations • 3 Dec 2016 • Yuankai Wu, Huachun Tan
An 1-dimension CNN is exploited to capture spatial features of traffic flow, and two LSTMs are utilized to mine the short-term variability and periodicities of traffic flow.