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781 results on '"Zhang, Botao"'

5. Prediction and Analysis of ECD for Deep Water Hydrate Formation Drilling with Riser

Author

Yu, Xiaodong, Li, Wentuo, Zhang, Botao, Zhang, Lei, Liu, Peikai, Lv, Pengbo, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Sun, Baojiang, editor, Sun, Jinsheng, editor, Wang, Zhiyuan, editor, Chen, Litao, editor, and Chen, Meiping, editor

Published
2024
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23. Generative Adversarial Networks with Physical Evaluators for Spray Simulation of Pintle Injector

Author

Ma, Hao, Zhang, Botao, Zhang, Chi, and Haidn, Oskar J.

Subjects
Physics - Fluid Dynamics
Abstract

Due to the adjustable geometry, pintle injectors are specially suitable for the liquid rocket engines which require a widely throttleable range. While applying the conventional computational fluid dynamics approaches to simulate the complex spray phenomena in the whole range still remains to be a great challenge. In this paper, a novel deep learning approach used to simulate instantaneous spray fields under continuous operating conditions is explored. Based on one specific type of neural networks and the idea of physics constraint, a Generative Adversarial Networks with Physics Evaluators (GAN-PE) framework is proposed. The geometry design and mass flux information are embedded as the inputs. After the adversarial training between the generator and discriminator, the generated field solutions are fed into the two physics evaluators. In this framework, mass conversation evaluator is designed to improve the training robustness and convergence. And the spray angle evaluator, which is composed of a down-sampling CNN and theoretical model, guides the networks generating the spray solutions more according with the injection conditions. The characterization of the simulated spray, including the spray morphology, droplet distribution and spray angle, is well predicted. The work suggests a great potential of the prior physics knowledge employment in the simulation of instantaneous flow fields., Comment: 24 pages, 9 figures, 2 tables

Published
2020
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42. Segmentation of glioblastomas via 3D FusionNet.

Author

Guo, Xiangyu, Zhang, Botao, Peng, Yue, Chen, Feng, and Li, Wenbin

Subjects
BRAIN tumors, DEEP learning, DATA augmentation, MAGNETIC resonance imaging, SAMPLE size (Statistics)
Abstract

Introduction: This study presented an end-to-end 3D deep learning model for the automatic segmentation of brain tumors. Methods: The MRI data used in this study were obtained from a cohort of 630 GBM patients from the University of Pennsylvania Health System (UPENN-GBM). Data augmentation techniques such as flip and rotations were employed to further increase the sample size of the training set. The segmentation performance of models was evaluated by recall, precision, dice score, Lesion False Positive Rate (LFPR), Average Volume Difference (AVD) and Average Symmetric Surface Distance (ASSD). Results: When applying FLAIR, T1, ceT1, and T2 MRI modalities, FusionNet-A and FusionNet-C the best-performing model overall, with FusionNet-A particularly excelling in the enhancing tumor areas, while FusionNet-C demonstrates strong performance in the necrotic core and peritumoral edema regions. FusionNet-A excels in the enhancing tumor areas across all metrics (0.75 for recall, 0.83 for precision and 0.74 for dice scores) and also performs well in the peritumoral edema regions (0.77 for recall, 0.77 for precision and 0.75 for dice scores). Combinations including FLAIR and ceT1 tend to have better segmentation performance, especially for necrotic core regions. Using only FLAIR achieves a recall of 0.73 for peritumoral edema regions. Visualization results also indicate that our model generally achieves segmentation results similar to the ground truth. Discussion: FusionNet combines the benefits of U-Net and SegNet, outperforming the tumor segmentation performance of both. Although our model effectively segments brain tumors with competitive accuracy, we plan to extend the framework to achieve even better segmentation performance. [ABSTRACT FROM AUTHOR]

Published
2024
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43. A Dynamic Head Gesture Recognition Method for Real-Time Human-Computer Interaction

Author

Xie, Jialong, Zhang, Botao, Chepinskiy, Sergey A., Zhilenkov, Anton A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Xin-Jun, editor, Nie, Zhenguo, editor, Yu, Jingjun, editor, Xie, Fugui, editor, and Song, Rui, editor

Published
2021
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45. Decoupling of Vibration and Temperature Signals of Fiber Bragg Grating Sensor

Author

LI Han, ZHANG Botao, WANG Junjie, SUN Yunda, GONG Shengjie

Subjects
nuclear power plant signal monitoring, fiber bragg grating (fbg) sensor, signal decoupling, Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Naval architecture. Shipbuilding. Marine engineering, VM1-989
Abstract

This paper uses a single fiber bragg grating (FBG) sensor to implement an experiment to measure vibration and temperature signals at the same time, and proposes a MATLAB-based decoupling method to separate vibration and temperature signals. The experimental results show that under the condition of single signal measurement, the static temperature measurement error of the FBG sensor is within ±0.4 ℃ and the relative error of the dynamic measurement of the main frequency of vibration is 0.5%. The FBG sensor measures the composite signal of vibration and temperature. The relative error of the main vibration frequency obtained by the decoupling method proposed in this experiment is 0.65%, the relative error of the vibration amplitude is 7.14%, and the temperature signal error is within ±3.3 ℃.

Published
2022
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48. Corrigendum to “Investigations into the antimicrobial behavior and cytotoxicity of purified detonation diamond nanoparticles and their assembled micro-/nano-structured films” [Mater. Lett. 363 (2024) 136294]

Author

Shen, Yan, primary, Li, Yihuan, additional, Wen, Yanghui, additional, Yang, Rui, additional, Liu, Yi, additional, Feng, Xiaohua, additional, Chen, Yunjie, additional, Liu, Xiaomei, additional, Zhang, Botao, additional, and Li, Hua, additional

Published
2024
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50. Semantic Knowledge Sharing Mechanism Based on Blockchain

Author

Zhang, Botao, Li, Xingzhou, Ren, Hui, Gu, Jinguang, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Liu, Yong, editor, Wang, Lipo, editor, Zhao, Liang, editor, and Yu, Zhengtao, editor

Published
2020
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