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Your search keyword '"Zhu, Fuping"' showing total 97 results
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97 results on '"Zhu, Fuping"'

4. Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification

Author

Liu, Qingshan, Hang, Renlong, Song, Huihui, Zhu, Fuping, Plaza, Javier, and Plaza, Antonio

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification. To this end, the optimal fusing weights for different convolutional layers are learned from the data itself. In remotely sensed scenes, the objects of interest exhibit different scales in distinct scenes, and even a single scene may contain objects with different sizes. To address this issue, we select the CNN with spatial pyramid pooling (SPP-net) as the basic deep network, and further construct a multi-scale ADPM model to learn complementary information from multi-scale images. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

Published
2016

5. Graph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval

Author

Sun, Yubao, Hang, Renlong, Liu, Qingshan, Zhu, Fuping, and Pei, Hucheng

Subjects
Computer Science - Learning
Abstract

In this paper, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval. First, we adopt a low rank representation (LRR) model to learn a powerful representation of the spectral response. Then, graph regularization is incorporated into the LRR model to capture the local structure information and the nonlinear property of the remote-sensing data. Since it is easy to acquire the rich satellite-retrieval results, we use them as a baseline to construct the graph. Finally, the learned feature representation is feeded into support vector machine (SVM) to retrieve AOD. Experiments are conducted on two widely used data sets acquired by different sensors, and the experimental results show that the proposed method can achieve superior performance compared to the physical models and other state-of-the-art empirical models., Comment: 16 pages, 6 figures

Published
2016
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18. Tao‐Hong‐Si‐Wu decoction reduces ischemia reperfusion rat myoblast cells calcium overloading and inflammation through the Wnt/IP3R/CAMKII pathway

Author

Wang Aijun, Li Wuping, Wang Linhua, Zhang Yi, Zhu Fuping, Pan Cheng-xi, and Zhou Fu-qiang

Subjects
Male, 0301 basic medicine, Frizzled, Ischemia, Gene Expression, chemistry.chemical_element, Inflammation, Calcium, Pharmacology, Biochemistry, Wnt-5a Protein, Proinflammatory cytokine, Myoblasts, Rats, Sprague-Dawley, 03 medical and health sciences, 0302 clinical medicine, Ca2+/calmodulin-dependent protein kinase, medicine, Animals, Inositol 1,4,5-Trisphosphate Receptors, Molecular Biology, Protein kinase C, Wnt signaling pathway, Proteins, Cell Biology, medicine.disease, 030104 developmental biology, chemistry, Reperfusion Injury, 030220 oncology & carcinogenesis, medicine.symptom, Calcium-Calmodulin-Dependent Protein Kinase Type 2, Drugs, Chinese Herbal, Phytotherapy, Signal Transduction
Abstract

Limb ischemia reperfusion (LIRI) injury is associated with serious local and systemic effects. Reperfusion may augment tissue injury in excess of that produced by ischemia alone. Calcium overloading and inflammation are considered to be two of the pathological mechanisms of limb ischemia/reperfusion (I/R) injury. Tao-Hong-Si-Wu decoction (THSWD) is a traditional Chinese herbal medicine with a powerful anti-inflammatory properties. We studied the probable restorative effect of THSWD on limb I/R-induced calcium overloading and inflammation in myoblast obtained from gastrocnemius muscle tissues of Sprague-Dawley rats (Frizzled Z5,a wnt5a blocker; KN-93, a calmodulin-dependent protein kinase II (CamkII) blocker; XeC, a IP3R blocker as positive controls). The simulated ischemia and reperfusion(I/R) solutions were used to imitate LIRI environment. The results showed that after I/R treatment, the secretion of proinflammatory factors (TNF-α and IL-1β) and Wnt5a/Ca2+ signal molecules (wnt5a, camkII, and IP3R) upregulated significantly, the Ca2+ concentration enhanced too in myoblast cells. THSWD pretreatment decreased the secretion of TNF-α and IL-1β, Ca2+ concentration; and abated the Wnt5a/Ca2+ signal molecules of wnt5a, camkII and IP3R expression activated by I/R injury; but could not abated the Wnt11 and protein kinase C (PKC) expression significantly, the results was similar with Frizzled Z5 treatment cells. Our research illustrated that THSWD may have a mitigating effect on LIRI targeting Wnt/IP3R/CAMKII but not Wnt/IP3R/PKC signaling pathway for the first time. This study may encourage the use of THSWD in the critical clinical settings with LIRI.

Published
2019

29. Graph Regularized Nonlinear Ridge Regression for Remote Sensing Data Analysis.

Author

Hang, Renlong, Liu, Qingshan, Song, Huihui, Sun, Yubao, Zhu, Fuping, and Pei, Hucheng

Abstract

In this paper, a graph regularized nonlinear ridge regression (RR) model is proposed for remote sensing data analysis, including hyper-spectral image classification and atmospheric aerosol retrieval. The RR is an efficient linear regression method, especially in handling cases with a small number of training samples or with correlated features. However, large amounts of unlabeled samples exist in remote sensing data analysis. To sufficiently explore the information in unlabeled samples, we propose a graph regularized RR (GRR) method, where the vertices denote labeled or unlabeled samples and the edges represent the similarities among different samples. A natural assumption is that the predict values of neighboring samples are close to each other. To further address the nonlinearly separable problem in remote sensing data caused by the complex acquisition process as well as the impacts of atmospheric and geometric distortions, we extend the proposed GRR into a kernelized nonlinear regression method, namely KGRR. To evaluate the proposed method, we apply it to both classification and regression tasks and compare with representative methods. The experimental results show that KGRR can achieve favorable performance in terms of predictability and computation time. [ABSTRACT FROM PUBLISHER]

Published
2017
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31. Application of the zero-crossing rate, LOFAR spectrum and wavelet to the feature extraction of passive sonar signals

Author

Zhu Fuping and Li Xueyao

Subjects
Engineering, Zero-crossing rate, business.industry, Feature extraction, Wavelet transform, Pattern recognition, LOFAR, Sonar, Signal, Wavelet, Computer vision, Artificial intelligence, Underwater, business
Abstract

In this paper, the features extraction of passive sonar signals and classification recognition of underwater target are introduced. Due to the complexity and non-stationary of underwater signals, the zero-cross ratio is first used to initially classify the noise signal; then the LOFAR spectrum reflecting non-stationary signal is extracted, and during which the wavelet transform is carried out for some classes of signals. Finally, a fuzzy ART neural network is constructed to carry out the classification. Results of the experiment show that, for six-class target 147 running environments, 5000 realistic data of ship, the mean correct ratio achieves 89%. The result obtained is satisfactory.

Published
2002

38. First sonic‐imaging AVA

Author

Zhu, Fuping, primary, Alexander, David, additional, Goossens, Peter, additional, Kurniawan, Hari, additional, Borland, William, additional, and Edwards, John, additional

Published
2007
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