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4,483 results on '"Zhang, Yanping"'

52. Robust Sentiment Analysis Model Based on Feature Representation in Uncertainty Domain

Author

CHEN Jie, LI Shuai, ZHAO Shu, ZHANG Yanping

Subjects
sentiment analysis, three-way decision, robustness, multi-granularity feature representation, feature fusion, Electronic computers. Computer science, QA75.5-76.95
Abstract

In the sentiment classification of text data, there are often some fuzzy data that are difficult to classify. Due to their uncertainty, these fuzzy data appear to be over fitted during model training, which affects the robustness of the model. The three-way decision theories divide the initial sample into deterministic domains and uncertain domains, and how to select appropriate features for representation in the uncertain domain where the fuzzy data is located for downstream tasks is the challenge of the three-way decision sentiment analysis models. To address this challenge, a robust sentiment analysis model (UFR-SA) based on feature representation of three-way decision uncertainty domains is proposed. Firstly, based on the three-way decision theory, the deterministic domain and the uncertain domain are divided. For fuzzy samples in the uncertain domain, heterogeneous sample point pairs are defined to construct hierarchical features. Secondly, a hierarchical feature fusion model is designed to incorporate the advantages of each granularity feature into a multi-layer perceptual network. Finally, a divide and conquer strategy is adopted for test samples in the deterministic domain and the uncertain domain. The deterministic domain data are represented by the original features, and the fuzzy data in the uncertain domain are represented by the fused robust features. Experimental results on SST-2, SST-5, and CR datasets show that UFR-SA effectively reduces the interference of fuzzy data on the model and outperforms the performance of state-of-the-art models.

Published
2023
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61. GTM: Gray Temporal Model for Video Recognition

Author

Zhang, Yanping and Yu, Yongxin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Data input modality plays an important role in video action recognition. Normally, there are three types of input: RGB, flow stream and compressed data. In this paper, we proposed a new input modality: gray stream. Specifically, taken the stacked consecutive 3 gray images as input, which is the same size of RGB, can not only skip the conversion process from video decoding data to RGB, but also improve the spatio-temporal modeling ability at zero computation and zero parameters. Meanwhile, we proposed a 1D Identity Channel-wise Spatio-temporal Convolution(1D-ICSC) which captures the temporal relationship at channel-feature level within a controllable computation budget(by parameters G & R). Finally, we confirm its effectiveness and efficiency on several action recognition benchmarks, such as Kinetics, Something-Something, HMDB-51 and UCF-101, and achieve impressive results.

Published
2021

79. Assessing River Water Quality Using Different Remote Sensing Technologies

Author

HOU Yikai, ZHANG Anbing, LYU Rulan, XUE Xiaoshuang, ZHANG Yanping, and PANG Jiyu

Subjects
multi-source remote sensing, random forest, small and medium-sized rivers, water quality parameters, Agriculture (General), S1-972, Irrigation engineering. Reclamation of wasteland. Drainage, TC801-978
Abstract

【Objective】 Remote sensing has become a prominent tool for monitoring and evaluating surface water quality across catchment and basin scales. This paper compares different remote sensing technologies for assessing water quality of terrestrial rivers. 【Method】 We took the Fuyang River in Handan City as an example. Spectral data in different seasons were acquired from three distinct remote sensing platforms: Sentinel-2 satellite images, unmanned aerial vehicles (UAVs) equipped with multispectral sensors, and ASD field spectrometers. The random forest (RF) algorithm was used to derive the inversion model from each platform to assess water quality. 【Result】 ① The results obtained from RF models for different platforms showed noticeable seasonal variation. Overall, the calculated results were most accurate in the summer, followed by spring, and were least reliable in winter. During summer, remote sensing data obtained from all three platforms can accurately estimate water quality parameters. In spring, UAV remote sensing images were sufficient. ② The RF model derived from the UAV multispectral data can effectively predict turbidity (Turb) and suspended solids (SS) of water i the river, regardless of seasons. ③ The RF model derived from the hyperspectral data was most accurate for estimating total nitrogen, with R2 >0.9 in spring. ④ Hyperspectral reflectivity curves for different seasons showed distinct variation due to the change in pollution level. In summer, reflectivity distribution curves display markedly spatial variation. The reflectivity values at each wavelength varied significantly, with pronounced changes in absorption and reflection. The reflectivity curves in spring and winter were similar, but showed pronounced changes in spring, regardless of the wavelength. 【Conclusion】 Remote sensing images obtained from different platforms offer a wealth of data for river water quality monitoring. They provide a new avenue for real-time river water quality assessment.

Published
2023
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96. JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets

Author

Chen, Jun, Yang, Guang, Khan, Habib, Zhang, Heye, Zhang, Yanping, Zhao, Shu, Mohiaddin, Raad, Wong, Tom, Firmin, David, and Keegan, Jennifer

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, 68T01
Abstract

Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifying atrial scars. The previous quantification of atrial scars relies on a two-phase segmentation for LA and atrial scars due to their large volume difference (unbalanced atrial targets). In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way. Firstly, JAS-GAN investigates an adaptive attention cascade to automatically correlate the segmentation tasks of the unbalanced atrial targets. The adaptive attention cascade mainly models the inclusion relationship of the two unbalanced atrial targets, where the estimated LA acts as the attention map to adaptively focus on the small atrial scars roughly. Then, an adversarial regularization is applied to the segmentation tasks of the unbalanced atrial targets for making a consistent optimization. It mainly forces the estimated joint distribution of LA and atrial scars to match the real ones. We evaluated the performance of our JAS-GAN on a 3D LGE CMR dataset with 192 scans. Compared with the state-of-the-art methods, our proposed approach yielded better segmentation performance (Average Dice Similarity Coefficient (DSC) values of 0.946 and 0.821 for LA and atrial scars, respectively), which indicated the effectiveness of our proposed approach for segmenting unbalanced atrial targets., Comment: Accepted by IEEE Journal of Biomedical and Health Informatics

Published
2021

97. Unsupervised KeyPhrase Extraction Based on Multi-granular Semantics Feature Fusion

Author

Chen, Jie, Hu, Hainan, Zhao, Shu, Zhang, Yanping, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Campagner, Andrea, editor, Urs Lenz, Oliver, editor, Xia, Shuyin, editor, Ślęzak, Dominik, editor, Wąs, Jarosław, editor, and Yao, JingTao, editor

Published
2023
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98. Multi-shot Prototype Contrastive Learning and Semantic Reasoning for Medical Image Segmentation

Author

Song, Yuhui, Du, Xiuquan, Zhang, Yanping, Xu, Chenchu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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99. Speed Fluctuation Suppression Strategy of PMSM Based on Improved Linear Active Disturbance Rejection Control

Author

Cui, Yangyang, Yin, Zhonggang, Luo, Peien, Zhang, Yanping, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Hu, Cungang, editor, and Cao, Wenping, editor

Published
2023
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