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1,355 results on '"Wu Yaping"'

1. Outline Design of an Atmospheric Corrosion Data Monitoring System

Author

Wang Qian, Fan Zhibin, Du Baoshuai, Zong Lijun, Mi Chunxu, and Wu Yaping

Subjects
atmospheric corrosion, corrosion monitoring, corrosion map, data collector, Environmental sciences, GE1-350
Abstract

Atmospheric corrosion monitoring and evaluation are important means for studying the mechanism and behavior of atmospheric corrosion of metals. In view of the long cycle, cumbersome process, and poor data quality of the traditional outdoor hanging method, an innovative atmospheric corrosion data monitoring system has been designed to detect atmospheric environment data and metal corrosion data on site, and display the detection data of the equipment’s service location to users in real-time in the form of a “dynamic corrosion map”. This can provide a differentiated design for the anti-corrosion of important equipment such as power grid transmission and transformation engineering components Provide effective data support for engineering life prediction and operation and maintenance. At present, the system has been piloted and promoted in some network provinces, achieving good social and economic benefits.

Published
2024
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2. Metal–organic framework-derived nanoporous titanium dioxide–heteropoly acid composites and its application in esterification

Author

Zhang Qiuyun, Luo Qizhi, Yang Xianju, Wu Yaping, Yang Binbin, Wang Jialu, and Zhang Yutao

Subjects
mil-125(ti), heteropoly acid, nanoporous, esterification, biodiesel, Chemistry, QD1-999
Abstract

MIL-125(Ti) metal–organic framework-derived nanoporous titanium dioxide–heteropoly acid (PW–TiO2) have been synthesized by a facile hydrothermal approach and subsequent calcination, and the PW–TiO2 catalysts were investigated using various characterization technologies including FTIR, XRD, SEM, TEM, N2-physisorption, and NH3-TPD. Further, we investigated the use of PW–TiO2 composites as heterogeneous acid catalysts for the catalytic esterification of oleic acid. Among all series of catalysts, the calcined PW–TiO2 at a temperature of 350°C showed excellent activity with a high conversion of 90.5% at 160°C for 4 h. More importantly, the catalyst could be simply separated via centrifugation and reused for six cycles and shows a relatively high conversion of 74.8%.

Published
2021
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17. Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence

Author

Qian, Chen, Gao, Yuncheng, Han, Mingyang, Wang, Zi, Ruan, Dan, Shen, Yu, Wu, Yaping, Zhou, Yirong, Wang, Chengyan, Jiang, Boyu, Tao, Ran, Wu, Zhigang, Wang, Jiazheng, Zhu, Liuhong, Guo, Yi, Kang, Taishan, Lin, Jianzhong, Gong, Tao, Yang, Chen, Fei, Guoqiang, Lin, Meijin, Guo, Di, Zhou, Jianjun, Wang, Meiyun, and Qu, Xiaobo

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

Diffusion magnetic resonance imaging (MRI) is the only imaging modality for non-invasive movement detection of in vivo water molecules, with significant clinical and research applications. Diffusion MRI (DWI) acquired by multi-shot techniques can achieve higher resolution, better signal-to-noise ratio, and lower geometric distortion than single-shot, but suffers from inter-shot motion-induced artifacts. These artifacts cannot be removed prospectively, leading to the absence of artifact-free training labels. Thus, the potential of deep learning in multi-shot DWI reconstruction remains largely untapped. To break the training data bottleneck, here, we propose a Physics-Informed Deep DWI reconstruction method (PIDD) to synthesize high-quality paired training data by leveraging the physical diffusion model (magnitude synthesis) and inter-shot motion-induced phase model (motion phase synthesis). The network is trained only once with 100,000 synthetic samples, achieving encouraging results on multiple realistic in vivo data reconstructions. Advantages over conventional methods include: (a) Better motion artifact suppression and reconstruction stability; (b) Outstanding generalization to multi-scenario reconstructions, including multi-resolution, multi-b-value, multi-undersampling, multi-vendor, and multi-center; (c) Excellent clinical adaptability to patients with verifications by seven experienced doctors (p<0.001). In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging., Comment: 23 pages, 16 figures

Published
2022

19. Quad-Net: Quad-domain Network for CT Metal Artifact Reduction

Author

Li, Zilong, Gao, Qi, Wu, Yaping, Niu, Chuang, Zhang, Junping, Wang, Meiyun, Wang, Ge, and Shan, Hongming

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

Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various methods were developed for CT metal artifact reduction over the past decades, including the latest dual-domain deep networks, remaining metal artifacts are still clinically challenging in many cases. Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate metal artifacts optimally without compromising structural subtleties. Our proposed quad-domain network for MAR, referred to as Quad-Net, takes little additional computational cost since the Fourier transform is highly efficient, and works across the four receptive fields to learn both global and local features as well as their relations. Specifically, we first design a Sinogram-Fourier Restoration Network (SFR-Net) in the sinogram domain and its Fourier space to faithfully inpaint metal-corrupted traces. Then, we couple SFR-Net with an Image-Fourier Refinement Network (IFR-Net) which takes both an image and its Fourier spectrum to improve a CT image reconstructed from the SFR-Net output using cross-domain contextual information. Quad-Net is trained on clinical datasets to minimize a composite loss function. Quad-Net does not require precise metal masks, which is of great importance in clinical practice. Our experimental results demonstrate the superiority of Quad-Net over the state-of-the-art MAR methods quantitatively, visually, and statistically. The Quad-Net code is publicly available at https://github.com/longzilicart/Quad-Net.

Published
2022
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36. High temporal resolution total-body dynamic PET imaging based on pixel-level time-activity curve correction

Author

Chen, Zixiang, Wu, Yaping, Zhang, Na, Shen, Yu, Zheng, Hairong, Liang, Dong, Wang, Meiyun, and Hu, Zhanli

Subjects
Physics - Medical Physics
Abstract

Dynamic positron emission tomography (dPET) is currently a widely used medical imaging technique for the clinical diagnosis, staging and therapy guidance of all kinds of human cancers. Higher temporal imaging resolution for the early stage of radiotracer metabolism is desired; however, in this case, the reconstructed images with short frame durations always suffer from a limited image signal-to-noise ratio (SNR) and unsatisfactory image spatial resolution. The appearance of uEXPLORER (United Imaging Healthcare, Inc.) with higher PET imaging sensitivity and resolution may help solving this problem. In this work, based on dynamic PET data acquired by uEXPLORER, we proposed a dPET processing method that denoises images with short frame durations via pixel-level time-activity curve (TAC) correction based on third-order Hermite interpolation (Pitch-In). The proposed method was validated and compared to several state-of-the-art methods to demonstrate its superior performance in terms of high temporal resolution dPET image noise reduction and imaging contrast. Higher stability and feasibility of the proposed Pitch-In method for future clinical application with high temporal resolution (HTR) dPET imaging can be expected., Comment: The manuscript contents 21 pages, 11 figures and 1 table

Published
2021

41. Annotation-efficient deep learning for automatic medical image segmentation

Author

Wang, Shanshan, Li, Cheng, Wang, Rongpin, Liu, Zaiyi, Wang, Meiyun, Tan, Hongna, Wu, Yaping, Liu, Xinfeng, Sun, Hui, Yang, Rui, Liu, Xin, Chen, Jie, Zhou, Huihui, Ayed, Ismail Ben, and Zheng, Hairong

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

Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications.

Published
2020
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45. A phase Ib trial of pembrolizumab plus paclitaxel or flat-dose capecitabine in 1st/2nd line metastatic triple-negative breast cancer

Author

Page, David B., Pucilowska, Joanna, Chun, Brie, Kim, Isaac, Sanchez, Katherine, Moxon, Nicole, Mellinger, Staci, Wu, Yaping, Koguchi, Yoshinobu, Conrad, Valerie, Redmond, William L., Martel, Maritza, Sun, Zhaoyu, Campbell, Mary B., Conlin, Alison, Acheson, Anupama, Basho, Reva, McAndrew, Philomena, El-Masry, Mary, Park, Dorothy, Bennetts, Laura, Seitz, Robert S., Nielsen, Tyler J., McGregor, Kimberly, Rajamanickam, Venkatesh, Bernard, Brady, Urba, Walter J., and McArthur, Heather L.

Published
2023
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