Search Results for author: Yinhai Wang

Found 21 papers, 7 papers with code

Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels

1 code implementation16 Sep 2022 Jan Oscar Cross-Zamirski, Guy Williams, Elizabeth Mouchet, Carola-Bibiane Schönlieb, Riku Turkki, Yinhai Wang

We propose WS-DINO as a novel framework to use weak label information in learning phenotypic representations from high-content fluorescent images of cells.

Knowledge Distillation Self-Supervised Learning

Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting

no code implementations19 Jun 2022 Meng-Ju Tsai, Zhiyong Cui, Hao Yang, Cole Kopca, Sophie Tien, Yinhai Wang

To better manage future roadway capacity and accommodate social and human impacts, it is crucial to propose a flexible and comprehensive framework to predict physical-aware long-term traffic conditions for public users and transportation agencies.

Management Time Series +1

TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer

no code implementations4 Feb 2022 Meixin Zhu, Simon S. Du, Xuesong Wang, Hao, Yang, Ziyuan Pu, Yinhai Wang

Through cross-attention between encoder and decoder, the decoder learns to build a connection between historical driving and future LV speed, based on which a prediction of future FV speed can be obtained.

Trajectory Prediction

Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection

no code implementations9 Dec 2021 Yifan Zhuang, Ziyuan Pu, Jia Hu, Yinhai Wang

Besides, the quantized IT-MN achieves an inference time of 0. 21 seconds per image pair on the edge device, which also demonstrates the potentiality of deploying the proposed model on edge devices as a highly efficient pedestrian detection algorithm.

Edge-computing Pedestrian Detection +1

Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications

no code implementations2 Aug 2020 Ruimin Ke, Zhiyong Cui, Yanlong Chen, Meixin Zhu, Hao Yang, Yinhai Wang

It is among the first efforts in applying edge computing for real-time traffic video analytics and is expected to benefit multiple sub-fields in smart transportation research and applications.

Autonomous Driving Edge-computing +2

CANet: Context Aware Network for 3D Brain Glioma Segmentation

1 code implementation15 Jul 2020 Zhihua Liu, Lei Tong, Long Chen, Feixiang Zhou, Zheheng Jiang, Qianni Zhang, Yinhai Wang, Caifeng Shan, Ling Li, Huiyu Zhou

Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning.

Brain Tumor Segmentation Tumor Segmentation

Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values

no code implementations24 May 2020 Zhiyong Cui, Ruimin Ke, Ziyuan Pu, Yinhai Wang

Further, comprehensive comparison results show that the proposed data imputation mechanism in the RNN-based models can achieve outstanding prediction performance when the model's input data contains different patterns of missing values.

Imputation Traffic Prediction

Graph Markov Network for Traffic Forecasting with Missing Data

2 code implementations10 Dec 2019 Zhiyong Cui, Longfei Lin, Ziyuan Pu, Yinhai Wang

Although missing values can be imputed, existing data imputation methods normally need long-term historical traffic state data.

Edge-computing Imputation +1

Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data

no code implementations1 Nov 2019 Ziyuan Pu, Shuo Wang, Chenglong Liu, Zhiyong Cui, Yinhai Wang

A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity.

Decision Making Friction +1

Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data

no code implementations1 Nov 2019 Ziyuan Pu, Zhiyong Cui, Shuo Wang, Qianmu Li, Yinhai Wang

The findings can help improve the prediction accuracy and efficiency of forecasting road surface friction using historical data sets with missing values, therefore mitigating the impact of wet or icy road conditions on traffic safety.

Friction

Personalized Context-Aware Multi-Modal Transportation Recommendation

no code implementations13 Oct 2019 Meixin Zhu, Jingyun Hu, Hao, Yang, Ziyuan Pu, Yinhai Wang

Also, results of the multinomial logit model show that (1) an increase in travel cost would decrease the utility of all the transportation modes; (2) people are less sensitive to the travel distance for the metro mode or a multi-modal option that containing metro, i. e., compared to other modes, people would be more willing to tolerate long-distance metro trips.

Learning-To-Rank

Phenotypic Profiling of High Throughput Imaging Screens with Generic Deep Convolutional Features

no code implementations15 Mar 2019 Philip T. Jackson, Yinhai Wang, Sinead Knight, Hongming Chen, Thierry Dorval, Martin Brown, Claus Bendtsen, Boguslaw Obara

While deep learning has seen many recent applications to drug discovery, most have focused on predicting activity or toxicity directly from chemical structure.

Drug Discovery

Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact

no code implementations5 Mar 2019 Ruimin Ke, Wan Li, Zhiyong Cui, Yinhai Wang

In this model, we first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial-temporal multi-channel matrices.

Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning

no code implementations3 Jan 2019 Meixin Zhu, Xuesong Wang, Yinhai Wang

This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.

Autonomous Driving reinforcement-learning +1

Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism

no code implementations24 Oct 2018 Zhengchao Zhang, Meng Li, Xi Lin, Yinhai Wang, Fang He

Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications.

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

2 code implementations20 Feb 2018 Zhiyong Cui, Kristian Henrickson, Ruimin Ke, Ziyuan Pu, Yinhai Wang

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks.

Traffic Prediction

Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

1 code implementation7 Jan 2018 Zhiyong Cui, Ruimin Ke, Ziyuan Pu, Yinhai Wang

In this paper, a deep stacked bidirectional and unidirectional LSTM (SBU- LSTM) neural network architecture is proposed, which considers both forward and backward dependencies in time series data, to predict network-wide traffic speed.

Time Series

A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation

no code implementations5 Jan 2018 Yunyi Liang, Zhiyong Cui, Yu Tian, Huimiao Chen, Yinhai Wang

The GAA is able to combine traffic flow theory with neural networks and thus improve the accuracy of traffic state estimation.

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