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185 results on '"Wu, Zehao"'

4. Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer

Author

Liu, Tong, Chen, Zhaowei, Yang, Yi, Wu, Zehao, and Li, Haowei

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Nowadays, deep learning techniques are widely used for lane detection, but application in low-light conditions remains a challenge until this day. Although multi-task learning and contextual-information-based methods have been proposed to solve the problem, they either require additional manual annotations or introduce extra inference overhead respectively. In this paper, we propose a style-transfer-based data enhancement method, which uses Generative Adversarial Networks (GANs) to generate images in low-light conditions, that increases the environmental adaptability of the lane detector. Our solution consists of three parts: the proposed SIM-CycleGAN, light conditions style transfer and lane detection network. It does not require additional manual annotations nor extra inference overhead. We validated our methods on the lane detection benchmark CULane using ERFNet. Empirically, lane detection model trained using our method demonstrated adaptability in low-light conditions and robustness in complex scenarios. Our code for this paper will be publicly available., Comment: Accepted by IV 2020

Published
2020

8. International Political Economy Analysis of Free Trade Area Construction Under the Background of Big Data

Author

Wu, Zehao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, 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, Sun, Songlin, editor, Hong, Tao, editor, Yu, Peng, editor, and Zou, Jiaqi, editor

Published
2022
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21. Research on a novel fault diagnosis method for gearbox based on matrix distance feature.

Author

Li, Jiangcheng, Dong, Limin, Zhang, Xiaotao, Liu, Fulong, Chen, Wei, and Wu, Zehao

Subjects
ROLLER bearings, GEARBOXES, FAULT diagnosis, DIAGNOSIS methods, ACOUSTIC emission
Abstract

Aiming at the problem of fault diagnosis and classification of rolling bearing and gear of gearboxes, a novel method based on matrix distance features of Gramian angular field (GAF) image is proposed based on sliding window compressible GAF transformation. The method converts the one-dimensional fault signal into a two-dimensional feature matrix and constructs the discrimination matrix of each fault category by establishing the mean value of the feature matrix of a priori samples. For the new sampled signal, after converting it into a two-dimensional feature matrix, the feature matrix is obtained. The fault classification is carried out by using the matrix distance between feature matrix and the discrimination matrix of each category. The method is validated by the test data of Case Western Reserve University and the acoustic emission data from a gearbox test bench. The classification accuracy is 99.17% and 95.71%, which presented the feasibility and effectiveness of the novel method proposed in this paper. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Development of Guidance Techniques for Regional Anesthesia: Past, Present and Future

Author

Wu,Zehao and Wang,Yun

Subjects
Journal of Pain Research
Abstract

Zehao Wu, Yun Wang Department of Anesthesiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, People’s Republic of ChinaCorrespondence: Yun WangDepartment of Anesthesiology, Beijing Chaoyang Hospital, Capital Medical University, No. 8 Gongti South Road, Chaoyang District, Beijing, People’s Republic of ChinaTel +86-13521611825Email wangyun129@ccmu.edu.cnAbstract: Regional anesthesia has been widely used in clinical practice. Over the past 30 years, various guidance techniques have been developed for regional anesthesia ranging from paresthesia progressing to neurostimulation and currently ultrasound guidance. Especially, the use of point-of-care ultrasound greatly enhances the success rate of regional anesthesia. However, the poor imaging quality of ultrasound in patients with obesity, fat infiltration, etc., limits the use of ultrasound. The combined use of ultrasound with neurostimulator, electromyography, pressure monitoring, etc. is advocated in order to facilitate regional anesthesia in this cohort of patients. The accumulated evidence has shown that the ultrasound combined with other techniques (UCOT) can help to solve the difficulties of puncture location caused by obesity, anatomical variation, and other factors when using ultrasound guidance alone. In recent years, with the development of medical image fusion and medical image artificial intelligence identification technology, guidance techniques for regional anesthesia have advanced considerably. To understand the advantages and disadvantages of the various guidance techniques for regional anesthesia developed in recent years and to guide their rational clinical application, this paper reviews these techniques, describing the progression from the early use of paresthesia to the latest UCOT, as well as the latest research on UCOT, and the prospects for the development of new guidance techniques for regional anesthesia.Keywords: guidance, neurostimulation, ultrasound, regional anesthesia, artificial intelligence

Published
2021

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