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102 results on '"Hu, Rongyao"'

3. Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan

Author

Zhu, Xiaofeng, Song, Bin, Shi, Feng, Chen, Yanbo, Hu, Rongyao, Gan, Jiangzhang, Zhang, Wenhai, Li, Man, Wang, Liye, Gao, Yaozong, Shan, Fei, and Shen, Dinggang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might convert to the severe stage, for designing effective treatment plan and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of high-dimensional data and learn the shared information across the classification task and the regression task. To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 422 chest computed tomography (CT) scans, where 52 cases were converted to severe on average 5.64 days and 34 cases were severe at admission. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.

Published
2020
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16. Supervised Feature Selection by Robust Sparse Reduced-Rank Regression

Author

Hu, Rongyao, Zhu, Xiaofeng, He, Wei, Zhang, Jilian, Zhang, Shichao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Li, Jinyan, editor, Li, Xue, editor, Wang, Shuliang, editor, Li, Jianxin, editor, and Sheng, Quan Z., editor

Published
2016
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17. Unsupervised Hypergraph Feature Selection with Low-Rank and Self-Representation Constraints

Author

He, Wei, Zhu, Xiaofeng, Li, Yonggang, Hu, Rongyao, Zhu, Yonghua, Zhang, Shichao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Li, Jinyan, editor, Li, Xue, editor, Wang, Shuliang, editor, Li, Jianxin, editor, and Sheng, Quan Z., editor

Published
2016
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18. Multigraph Fusion for Dynamic Graph Convolutional Network

Author

Gan, Jiangzhang, Hu, Rongyao, Mo, Yujie, Kang, Zhao, Peng, Liang, Zhu, Yonghua, and Zhu, Xiaofeng

Abstract

Graph convolutional network (GCN) outputs powerful representation by considering the structure information of the data to conduct representation learning, but its robustness is sensitive to the quality of both the feature matrix and the initial graph. In this article, we propose a novel multigraph fusion method to produce a high-quality graph and a low-dimensional space of original high-dimensional data for the GCN model. Specifically, the proposed method first extracts the common information and the complementary information among multiple local graphs to obtain a unified local graph, which is then fused with the global graph of the data to obtain the initial graph for the GCN model. As a result, the proposed method conducts the graph fusion process twice to simultaneously learn the low-dimensional space and the intrinsic graph structure of the data in a unified framework. Experimental results on real datasets demonstrated that our method outperformed the comparison methods in terms of classification tasks.

Published
2024
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22. Multi-scale Graph Fusion for Co-saliency Detection

Author

Hu, Rongyao, Deng, Zhenyun, and Xiaofeng Zhu

Subjects
General Medicine
Abstract

The key challenge of co-saliency detection is to extract discriminative features to distinguish the common salient foregrounds from backgrounds in a group of relevant images. In this paper, we propose a new co-saliency detection framework which includes two strategies to improve the discriminative ability of the features. Specifically, on one hand, we segment each image to semantic superpixel clusters as well as generate different scales/sizes of images for each input image by the VGG-16 model. Different scales capture different patterns of the images. As a result, multi-scale images can capture various patterns among all images by many kinds of perspectives. Second, we propose a new method of Graph Convolutional Network (GCN) to fine-tune the multi-scale features, aiming at capturing the common information among the features from all scales and the private or complementary information for the feature of each scale. Moreover, the proposed GCN method jointly conducts multi-scale feature fine-tune, graph learning, and feature learning in a unified framework. We evaluated our method on three benchmark data sets, compared to state-of-the-art co-saliency detection methods. Experimental results showed that our method outperformed all comparison methods in terms of different evaluation metrics.

Published
2021

23. Graph learning and its applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

Author

Xiaofeng, Zhu, Hu, Rongyao, Xiaofeng, Zhu, and Hu, Rongyao

Abstract

Since graph features consider the correlations between two data points to provide high-order information, i.e., more complex correlations than the low-order information which considers the correlations in the individual data, they have attracted much attention in real applications. The key of graph feature extraction is the graph construction. Previous study has demonstrated that the quality of the graph usually determines the effectiveness of the graph feature. However, the graph is usually constructed from the original data which often contain noise and redundancy. To address the above issue, graph learning is designed to iteratively adjust the graph and model parameters so that improving the quality of the graph and outputting optimal model parameters. As a result, graph learning has become a very popular research topic in traditional machine learning and deep learning. Although previous graph learning methods have been applied in many fields by adding a graph regularization to the objective function, they still have some issues to be addressed. This thesis focuses on the study of graph learning aiming to overcome the drawbacks in previous methods for different applications. We list the proposed methods as follows. • We propose a traditional graph learning method under supervised learning to consider the robustness and the interpretability of graph learning. Specifically, we propose utilizing self-paced learning to assign important samples with large weights, conducting feature selection to remove redundant features, and learning a graph matrix from the low dimensional data of the original data to preserve the local structure of the data. As a consequence, both important samples and useful features are used to select support vectors in the SVM framework. • We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the discrete wavelet transform and Pearson correla

Published
2022

29. Effect of Al Y gradient coating on hot corrosion resistance of γ-TiAl alloy at different temperatures

Author

Hao Lin, Zheng Ding, Qiang Miao, Wenping Liang, Lijia Yu, Hu Rongyao, and Yanlin Jia

Subjects
Materials science, Alloy, Oxide, General Physics and Astronomy, 02 engineering and technology, Surfaces and Interfaces, General Chemistry, Substrate (electronics), Activation energy, engineering.material, Sputter deposition, 010402 general chemistry, 021001 nanoscience & nanotechnology, Condensed Matter Physics, 01 natural sciences, Thermal expansion, 0104 chemical sciences, Surfaces, Coatings and Films, Corrosion, chemistry.chemical_compound, Coating, chemistry, engineering, Composite material, 0210 nano-technology
Abstract

It is generally accepted that the crack is appeared when the thermal stress is accumulated which caused by the mismatch of coefficient of thermal expansion, through which oxygen will severely destroy the substrate at high temperature environments. Therefore, the coatings with gradient structure to reduce thermal expansion mismatch should greatly improve their service stability. Here Al Y gradient coating is prepared on γ-TiAl alloy by magnetron sputtering. The results show that the hot corrosion resistance of γ-TiAl alloy could be greatly improved, and the activation energy at (750 °C–950 °C) can be calculated to be 126.88 KJ/mol for the coating. The Al2O3 and Y3Al5O12 (YAG) are the primary phases after hot corrosion, thus providing efficient protection at 750, 850 °C. At 950 °C, the consumption of oxide layers is accelerated due to the increased temperature so that some corrosive elements (such as S and Na) unavoidably invade into the scale. The improvement of hot corrosion resistance primarily owes to the existence of Y preferentially oxidizes Al to form α-Al2O3, and the effect of grain refinement of YAG phases.

Published
2019

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