Search

Your search keyword '"Shi, Yanyan"' showing total 761 results
Start Over Author "Shi, Yanyan"
761 results on '"Shi, Yanyan"'

105. Simulation Research of Spray Lubrication and Optimization of Nozzle Position in a Complex Gearbox

Author

Qian Xueling, Hu Yumei, Zhou Yuancong, Zhang Bo, Chen Guang, and Shi Yanyan

Subjects
Complex gearbox, High-speed gear, Spray lubrication, Experimental rig, Numerical simulation, Single-phase flow, Optimum spray angle, Mechanical engineering and machinery, TJ1-1570
Abstract

The numerical simulation of spray lubrication and improvement of lubricant condition in a complex gearbox are researched. An experimental rig for spray lubrication of a pair of high-speed gears is set up and relevant experiments are carried out firstly. The experimental results are used to verify the correctness of numerical simulation method. Then numeric model of spray lubrication in a complex gearbox is established and simulation as well as analysis are conducted. The results show that overlong spray distance,angle of end face and jet flow on out of mesh side make against to lubrication. Then optimization of spray angle based on single-phase flow computational method is studied. Result show that,there is a weakest trace of air in the center plane of meshed gears. And if the axis of the nozzle lays on this trace,the meshed gears will have their best lubricant performance.

Published
2018
Full Text
View/download PDF

121. Effects of interventions on the screening behavior in female first‐degree relatives of breast cancer patients: A systematic review.

Author

Shi, Yanyan, Ma, Zhuyue, Yao, Shanshan, Lu, Ningning, Wu, Yuqing, and Cheng, Fang

Subjects
*FAMILIES & psychology, *BEHAVIORAL assessment, *BREAST tumor diagnosis, *ONLINE information services, *MEDICAL databases, *CINAHL database, *PROBLEM solving, *SOCIAL support, *BREAST self-examination, *EARLY detection of cancer, *MAMMOGRAMS, *HEALTH behavior, *MEDLINE, *HEALTH promotion
Abstract

Background: Women are more likely to develop breast cancer if their first‐degree relatives (FDRs) have the disease, but they are often unaware of their individual risk and conduct screening behaviors. Objective: This study aimed to evaluate the effectiveness of interventions in increasing breast self‐examination, clinical breast examination, and mammography rates in FDRs of breast cancer patients. Methods: We selected randomized clinical trials and quasi‐experimental studies in eight databases. Interventions in each study were categorized as "promising", or "non‐promising" according to whether they led to a positive change in screening behaviors. Interventions were also coded using the Behavioral Change Techniques (BCTs) Taxonomy and a promise ratio calculated for each. BCTs with a promise ratio ≥2 was classified as "promising". Results: Thirteen studies with 21 different BCTs were included. The most frequent BCTs were "Prompts/cues", "Credible source", and "Instructions on how to perform the behavior". Seven BCTs had a promise ratio of ≥2 and the four most promising were "Information about health consequences" (promise ratio = 6), "Problem solving" (promise ratio = 4), "Demonstration of the behavior" (promise ratio = 4), and "Adding objects to the environment" (promise ratio = 4). Conclusions: This review indicated an overall weak use of theory, and an insufficient description of several interventions to support the assessment of how specific BCTs were activated. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

128. Individualized diagnosis and eradication therapy for Helicobacter pylori infection based on gene detection of clarithromycin resistance in stool specimens: A systematic review and meta‐analysis

Author

Ren, Xinlu, primary, Shi, Yanyan, additional, Suo, Baojun, additional, Yao, Xingyu, additional, Lu, Haoping, additional, Li, Cailing, additional, Zhang, Yuxin, additional, Zhou, Liya, additional, Tian, Xueli, additional, and Song, Zhiqiang, additional

Published
2023
Full Text
View/download PDF

143. Design of new metamaterial with negative permeability for efficient wireless power transfer.

Author

Wang, Meng, Wang, Mengmeng, Shi, Yanyan, Guo, Jingjing, Song, Guangcheng, and Yin, Renliang

Subjects
WIRELESS power transmission, METAMATERIALS, PERMEABILITY
Abstract

Summary: To solve the problem that the efficiency of a wireless power transfer system decreases with increasing transmission distance, metamaterial (MTM) has offered an effective solution. In this paper, a novel compact MTM with negative permeability is designed to improve the transfer efficiency of wireless power transfer at the operating frequency of 13.56 MHz. By placing the MTM slab at different positions between coils, the transmission coefficient of the wireless power transfer system with one MTM slab is calculated. Also, the impact of transmission distance is studied. Based on the two‐port network method, the transfer efficiency of wireless power transfer is respectively calculated for the system with one MTM slab and with two MTM slabs. The results demonstrate that transfer efficiency at the transmission distance of 150, 200, and 250 mm are, respectively, 82.4%, 67.8%, and 47.3% when one MTM slab is introduced. For the system with two MTM slabs, the corresponding transfer efficiency is 84.9%, 77.3%, and 66.1% separately. Compared with the system without MTM, it is found that there is a large improvement in transfer efficiency especially when two MTM slabs are positioned between coils at the transmission distance of 250 mm. The magnetic field distribution of the system with one or two MTM slabs introduced is investigated. The results show that magnetic coupling between coils is strengthened, which is favorable for transfer efficiency enhancement. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

144. Inadequate Data Augmentation With Convolutional Neural Network in Brain Electrical Impedance Tomography

Author

Shi, Yanyan, Yang, Ke, Wang, Meng, Su, Hao, Yang, Bin, and Fu, Feng

Abstract

Electrical impedance tomography (EIT) is a potential technique for imaging intracranial hemorrhage. In general, 16 electrodes are used in the brain EIT. In some situations, it may be not possible to attach so many electrodes. This reduces the number of measurements and causes poor reconstruction quality. In addition, the skull has poor conductivity which obstructs the injection of current and degrades the quality of the reconstructed image. Thus, inadequate boundary measurement caused by a reduced number of electrodes is a great challenge in brain EIT. To address this problem, a novel approach based on a convolutional neural network is proposed for inadequate data augmentation when only eight electrodes can be equipped. The proposed method enables the establishment of a nonlinear relationship between the inadequate voltage data and the desired augmented data. The voltage data augmented for the eight-electrode brain EIT are compared with the measured voltage of the brain EIT equipped with 16 electrodes. It is found that the two groups of data are very similar. The effectiveness of the proposed approach is further confirmed by imaging hemorrhage in the brain tissue layer. Images reconstructed for various cases are compared with the results, which use directly measured voltage. The qualitative evaluation as well as quantitative analysis demonstrates that the proposed approach greatly enhances the reconstruction quality in the case of inadequate measurement. In summary, the voltage data directly measured from the eight-electrode brain EIT can be well-augmented with the proposed method.

Published
2024
Full Text
View/download PDF

145. Sequence Matching for Image-Based UAV-to-Satellite Geolocalization

Author

Wang, Zhen, Shi, Dianxi, Qiu, Chunping, Jin, Songchang, Li, Tongyue, Shi, Yanyan, Liu, Zhe, and Qiao, Ziteng

Abstract

Unmanned aerial vehicle (UAV)-to-satellite geolocalization offers accurate drift-free navigation in the absence of external positioning signals. Increased deep-learning-based approaches have demonstrated their potential for high accuracy by framing the problem as a one-to-all retrieval task. However, in real-world scenario, the problem is not just a one-to-all retrieval task, which leads to a gap between research and applications. Based on this observation, we attempt to look closer to the problem instead of designing sophisticated network architectures or objective functions. In this study, we proposed a flexible and simple coarse-to-fine sequence-matching solution with targeted joint use of deep learning and classical machine learning approaches. Our goal is to improve geolocalization accuracy by matching UAV images with a few relevant reference image patches instead of all images. To this end, we first coarsely constructed a sequence of reference satellite image patches corresponding to the UAV trajectory, in which we proposed a deep feature- and manifold learning-based image-sorting method. Once the reference satellite patches are sorted and aligned with the UAV trajectory, the reference sequence is determined. Given a query UAV frame, the search area can be decreased from 2-D to 1-D. In particular, both deep-learning-based and classical image-matching algorithms can provide competitive accuracy when integrating sequence constraints. We demonstrate that classical manifold learning-based and image-matching methods perform exceptionally well for UAV-to-satellite geolocalization when utilized jointly with suitable deep-learning techniques. We validated the approach’s unique outperformance on two challenging and realistic UAV-to-satellite geolocalization datasets. Dataset, code, and models are available for research purposes at: https://seqmatch.geovisuallocalization.com/.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results