Search

Your search keyword '"Yang, Wenyu"' showing total 1,843 results
Start Over Author "Yang, Wenyu"
1,843 results on '"Yang, Wenyu"'

2. CATSNet: a context-aware network for Height Estimation in a Forested Area based on Pol-TomoSAR data

Author

Yang, Wenyu, Vitale, Sergio, Aghababaei, Hossein, Ferraioli, Giampaolo, Pascazio, Vito, and Schirinzi, Gilda

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Tropical forests are a key component of the global carbon cycle. With plans for upcoming space-borne missions like BIOMASS to monitor forestry, several airborne missions, including TropiSAR and AfriSAR campaigns, have been successfully launched and experimented. Typical Synthetic Aperture Radar Tomography (TomoSAR) methods involve complex models with low accuracy and high computation costs. In recent years, deep learning methods have also gained attention in the TomoSAR framework, showing interesting performance. Recently, a solution based on a fully connected Tomographic Neural Network (TSNN) has demonstrated its effectiveness in accurately estimating forest and ground heights by exploiting the pixel-wise elements of the covariance matrix derived from TomoSAR data. This work instead goes beyond the pixel-wise approach to define a context-aware deep learning-based solution named CATSNet. A convolutional neural network is considered to leverage patch-based information and extract features from a neighborhood rather than focus on a single pixel. The training is conducted by considering TomoSAR data as the input and Light Detection and Ranging (LiDAR) values as the ground truth. The experimental results show striking advantages in both performance and generalization ability by leveraging context information within Multiple Baselines (MB) TomoSAR data across different polarimetric modalities, surpassing existing techniques., Comment: Submitted to IEEE TGRS, under review

Published
2024

7. PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

Author

Cong, Runmin, Yang, Wenyu, Zhang, Wei, Li, Chongyi, Guo, Chun-Le, Huang, Qingming, and Kwong, Sam

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

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement sub-network (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics., Comment: 8 pages, 4 figures, Accepted by IEEE Transactions on Image Processing 2023

Published
2023

15. Study on the Influence Law of Indoor Temperature Conditions on Formaldehyde Diffusion: A Case Study of a Residential Building in China

Author

Gao, Tenghua, Zhang, Yu, Yang, Wenyu, 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, Tan, Kay Chen, Series Editor, Wang, Yi, editor, Yu, Tao, editor, and Wang, Kesheng, editor

Published
2024
Full Text
View/download PDF

16. Simulation and Engineering Practice of the Mine Ventilation and Refrigeration Engineering in a Hot Mine Based on the Assistance of Ventsim

Author

Xiao, Yue, Zhang, Yu, Yang, Wenyu, 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, Tan, Kay Chen, Series Editor, Wang, Yi, editor, Yu, Tao, editor, and Wang, Kesheng, editor

Published
2024
Full Text
View/download PDF

17. Shared Nearest Neighbor Calibration for Few-Shot Classification

Author

Qi, Rundong, Ning, Sa, Jiang, Yong, Zhang, Yuwei, Yang, Wenyu, 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, Liu, Qingshan, editor, Wang, Hanzi, editor, Ma, Zhanyu, editor, Zheng, Weishi, editor, Zha, Hongbin, editor, Chen, Xilin, editor, Wang, Liang, editor, and Ji, Rongrong, editor

Published
2024
Full Text
View/download PDF

18. PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars

Author

Hong, Shuxin, Zou, Zhiqiang, Luo, A-Li, Kong, Xiao, Yang, Wenyu, and Chen, Yanli

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415,930 quasars from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |{\Delta}z| = |(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and demonstrated a 4.04% increase in accuracy. With the help of the generated spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach 84.45% of the total test samples, whereas it reaches 80.41% for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown to decreases from 0.1332 to 0.1235. Our method has the potential to be generalized to other astronomical data analyses such as galaxy classification and redshift prediction. The algorithm code can be found at https://github.com/HongShuxin/PhotoRedshift-MML ., Comment: 10 pages, 8 figures, accepted for publication in MNRAS

Published
2022
Full Text
View/download PDF

22. Research on a digital twin driven virtual debugging method for roadway automatic forming cutting

Author

ZHANG Xuhui, LIU Yanhui, YANG Wenjuan, ZHANG Chao, DU Yuyang, YANG Junhao, and YANG Wenyu

Subjects
roadway automatic forming cutting, digital twin, virtual debugging, virtual sensors, virtual real coordinate transformation, Mining engineering. Metallurgy, TN1-997
Abstract

In response to the problems of long debugging cycle, high debugging cost, high safety risk, and difficult quality evaluation in the current automatic forming and cutting control of roadways, a digital twin driven virtual debugging method for roadway automatic forming and cutting is proposed. Firstly, by using real-time appearance mapping (RTAP-MAP) technology to reconstruct the three-dimensional environment of the roadway, a control system model of the roadheader is constructed to form a virtual debugging environment. The virtual sensor technology is used to achieve precise mapping from physical space to virtual environment state. A performance evaluation method for roadway automatic forming cutting has been established to address the problem of difficulty in quantitatively evaluating the quality of section forming. Based on the recording of the section forming cutting control process in the data transmission center, the evaluation indicators of section forming precision, cutting efficiency and the number of oil cylinder switches, hard rock cutting adjustment, and over excavation and under excavation are mainly calculated. This provides precise feedback signals for the iterative optimization of deep learning algorithms. An automatic cutting control strategy that integrates reinforcement learning is proposed to improve the adaptability and precision of automated operations. To verify the effectiveness and accuracy of the virtual debugging method, an automatic control experimental platform for roadheader is built. The virtual debugging system is applied to the automatic control program for forming cutting of excavation roadway. The virtual simulation results show the following points. ① The maximum positioning errors of the X, Y, and Z axes of the debugged software at the control key points are 74.8, 72.93, 123.67 mm, respectively. It indicates that the positioning precision of the virtual debugging method meets the performance requirements. ② The trajectory of the virtual prototype is basically consistent with that of the physical prototype, indicating that this debugging method has achieved mapping to the physical space. The application result shows the following points. ① The reinforcement learning controller adapts to complex environments in virtual excavation testing, effectively converts virtual sensor inputs into precise control instructions, and verifies the feasibility of simulation reality transfer training. By processing real-time feedback on excavation precision and avoiding over excavation and under excavation, the controller learns and optimizes the strategy. ② The improved section forming cutting control performance has been improved. According to the control quantity timestamp records in the database, it takes 126 seconds, which is 8 seconds less than before the improvement. ③ After improvement, the maximum error in tracking the end trajectory of the cutting section is 6.0 mm, which is 0.3 mm lower than before. This avoids the under excavation caused by the shaking of the cutting trajectory and makes the trajectory and section smoother.

Published
2024
Full Text
View/download PDF

49. Expert consensus on postoperative rehabilitation nursing of patients with head and neck cancer

Author

GU Fen, WANG Yueping, YANG Wenyu, ZHAO Xiaomei, TANG Yan, SHEN Shukun, MAO Yan, ZHANG Jinfeng, WU Yifan, ZHANG Yuanyuan, YANG Yue, ZHANG Jianchun, YU Hong, WANG Lan, HAO Guihua, and HOU Lili

Subjects
head and neck cancer, rehabilitation nursing, clinical practice, expert consensus, Medicine
Abstract

The location and size of tumors, treatment methods and prognosis of patients with head and neck cancer can seriously affect their oral function and neck activity, thereby affecting daily activities such as eating, speech and upper limb movement. Early rehabilitation after head and neck cancer surgery can accelerate functional recovery, alleviate discomfort symptoms, improve quality of life, and reduce unnecessary rehabilitation or treatment measures. Developing a clinical rehabilitation nursing pathway for head and neck cancer, forming personalized rehabilitation plans, and conducting early and effective nursing interventions are currently one of the key points of clinical work for patients with head and neck cancer. At present, domestic and foreign guidelines or consensus pays less attention to the impairments of speech function, chewing and swallowing function, neck and shoulder function etc., and lacks a systematic and comprehensive rehabilitation nursing guide or consensus to provide practical guidance for the care of patients with head and neck cancer. Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine organized relevant experts from Beijing, Shanghai, Sichuan, Shaanxi, Zhejiang and Anhui to draft Expert consensus on postoperative rehabilitation nursing of patients with head and neck cancer basing on previous literature and clinical nursing skills and experiences, of which the aim is to provide guidance for those patients in the aspects of oral care, nutritional support, flap donor area care, care after tracheotomy, chewing and swallowing rehabilitation, speech function rehabilitation, neck and shoulder function rehabilitation, restricted mouth opening rehabilitation, risk identification and prevention and follow-up.

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results