Search

Your search keyword '"Dong, Chaofan"' showing total 15 results
Start Over Author "Dong, Chaofan"
15 results on '"Dong, Chaofan"'

2. Equivalent Thermal Conductivity Improvement of Stator Winding and Thermal Benefit for Air-Cooled Electrical Aviation Machine

Author

Hu, Xuanyang, Dong, Chaofan, Qian, Yuping, Zhang, Yangjun, Wang, Changjiang, Zhuge, Weilin, 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, and Chinese Society of Aeronautics and Astronautics, editor

Published
2023
Full Text
View/download PDF

4. Thermal Conductivity of Stabilized Loess with Different Types of Lignin.

Author

Zhang, Wuyu, Dong, Chaofan, and Lin, Cheng

Subjects
*SOIL mineralogy, *THERMAL conductivity, *SOIL stabilization, *SOIL particles, *SCANNING electron microscopy
Abstract

Lignin, an abundant biopolymer derived from plants, is a green binder for stabilizing soil. This study investigates the thermal conductivity of lignin [e.g., sodium ignosulfonate (SL), calcium lignosulfonate (CL), and lignin fiber (LF)] and lignin-stabilized loess. The effects of the source of loess samples, lignin content, curing time, water content, and dry density on the thermal conductivity of the stabilized soils were experimentally evaluated. Furthermore, mineralogy and microstructure of the stabilized loess were investigated using X-ray diffraction, scanning electron microscopy, and mercury intrusion porosimetry tests to provide insights into the mechanisms of lignin-based soil stabilization. The test results showed that lignin had a lower thermal conductivity than water and soil minerals. The addition of lignin reduced the thermal conductivity of loess, with CL and SL causing a slightly greater reduction than LF. Mixing lignin with loess did not generate new crystalline material. The incorporation of CL and SL altered the microstructure of loess, resulting in a densely packed structure with distinct particle bonds and intra-aggregate pores. In contrast, LF provide microscale reinforcement for the soil particles, presenting a loose structure with dominant interaggregate pores. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

5. Adaptive Modulation and Coding for Underwater Acoustic OTFS Communications Based on Meta-Learning

Author

Jing, Lianyou, Dong, Chaofan, He, Chengbing, Shi, Wentao, Wang, Han, and Zhou, Yi

Abstract

This letter proposes an adaptive modulation and coding (AMC) scheme based on deep learning for underwater acoustic (UWA) communications. To achieve good communication performance in fast time-varying UWA channels, the proposed AMC scheme is implemented on the orthogonal time-frequency space (OTFS) modulation system. We design an end-to-end deep convolutional neural network (CNN) to capture the channel features and determine the optimal modulation and coding scheme. Additionally, we utilize a meta-learning algorithm to address environment mismatch in real-world UWA applications. This algorithm effectively adapts the CNN model from a given UWA environment to a new UWA environment with only a small amount of data. The performance of the proposed scheme is verified through real-world measured channels. Simulation results demonstrate that the proposed method outperforms existing machine learning-based AMC and fixed modulation and coding schemes in various UWA scenarios, offering better communication throughput and stronger learning capabilities.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results