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630 results on '"Wang, Jiaquan"'

3. Analysis of Detection Performance of Hainan VLF/LF Full Waveform Lightning Location Network

Author

Dong, Xiaoyan, Yang, Qingliu, Wang, Jiaquan, Zhou, Xiao, Xiao, Fang, Ma, Qiming, 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, Yang, Qingxin, editor, Li, Jian, editor, Xie, Kaigui, editor, and Hu, Jianlin, editor

Published
2023
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5. Variational Auto-Encoder Based Approximate Bayesian Computation Uncertian Inverse Method for Sheet Metal Forming Problem

Author

Wang, Jiaquan, Zeng, Yang, Jiang, Xinchao, Wang, Hu, Li, Enying, and Li, Guangyao

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

In this study, an image-assisted Approximate Bayesian Computation (ABC) parameter inverse method is proposed to identify the design parameters. In the proposed method, the images are mapped to a low-dimensional latent space by Variational Auto-Encoder (VAE), and the information loss is minimized by network training. Therefore, an effective trade-off between information loss and computational cost can be achieved by using the latent variables of VAE as summary statistics of ABC, which overcomes the difficulty of selecting summary statistics in the ABC. Besides, for some practical engineering problems, processing the images as objective function can effective show the response result. Meanwhile, the relationship between design parameters and the latent variables is constructed by Least Squares Support Vector Regression (LSSVR) surrogate model. With the well-constructed LSSVR model, the simulation coefficient vectors under given parameters will be determined effectively. Then, the parameters to be identified are determined by comparing the simulated and observed coefficient vectors in ABC. Finally, a sheet forming problem is investgated by the suggested method. The material parameters of the blank and the process parameters of the forming process are identified. Results show that the method is feasibility and effective for the identification of sheet forming parameters.

Published
2019

9. Classification of Lightning Electric Field Waveform Based on Deep Residual One-Dimensional Convolutional Network

Author

Gao, Chao, Wang, Jiaquan, Zhou, Xiao, Xiao, Fang, Ma, Qiming, 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, Meng, Hongying, editor, Lei, Tao, editor, Li, Maozhen, editor, Li, Kenli, editor, Xiong, Ning, editor, and Wang, Lipo, editor

Published
2021
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10. Pullout behavior of geogrid reinforcement in calcareous sand.

Author

Xu, Liangjie, Wang, Ren, Chen, Jianfeng, Wang, Jiaquan, Xu, Dongsheng, and Meng, Qingshan

Subjects
INTERFACIAL friction, INTERNAL friction, SPECIFIC gravity, SHEAR strength
Abstract

This study conducts a series of large-scale pullout tests to explore the pullout behavior of the biaxial geogrid-calcareous sand interface. It investigates the effects of normal stress, relative density, and embedded length on the interface pullout behavior of geogrid reinforcement in calcareous sand. In addition, this research examines changes in interface shear strength and the interface friction coefficient. The interface interaction mechanism of geogrid reinforcement in calcareous sand and the evolution of pullout force-clamp displacement curves are determined. The results indicated that the pullout force-clamp displacement curve primarily exhibits softening and hardening behaviors. The passive resistance of the geogrid mesh significantly influences the softening behavior, while shear friction predominantly affects the hardening behavior. The interface friction coefficient ranged from 0.28 to 0.88 for embedded lengths of 300 − 460 mm. The effects of relative density and embedded length on interface cohesion are more pronounced than those on the interface friction angle. When the embedded length increased from 300 to 460 mm, the rise in interface cohesion ranged from 42.0% to 47.7%, whereas the increase in internal friction angle ranged from 9.5% to 14.5%. [ABSTRACT FROM AUTHOR]

Published
2024
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47. THE EFFECTS OF TEACHING OF TRANSFORMATIVE LEARNING ON UNIVERSITY STUDENTS’ ENGLISH LEARNING AUTONOMY IN CHINA

Author

Wang Jiaquan, Hiew Wendy, and Suyansah Swanto

Subjects
General Earth and Planetary Sciences, General Environmental Science
Abstract

This study aims to study the effects of implementing the Teaching of Transformative Learning (TTL) on non-English major students’ English Learning Autonomy (ELA) specifically in an English reading course at a university in China. The main features of TTL were embodied in the critical reflection and the rational discourse on the basis on equal and friendly classroom atmosphere. The study utilized a questionnaire on learning autonomy as its research method to collect data from 116 students in the experimental class. Twenty-three items in the questionnaire investigated five aspects of the students’ ELA. The means of the post-test and pretest of the experimental class students’ ELA revealed a significant difference in the TTL. It indicated that the TTL had a good effect on improving the university students’ ELA in the present study. The study hopes to provide a beneficial reference for other scholars who are interested in improving students’ learning autonomy in their teaching practice.

Published
2022

48. LEPF-Net: Light Enhancement Pixel Fusion Network for Underwater Image Enhancement

Author

Wang, Jiaquan Yan, Yijian Wang, Haoyi Fan, Jiayan Huang, Antoni Grau, and Chuansheng

Subjects
underwater image enhancement, light enhancement curve, pixel fusion, adaptive feature weights
Abstract

Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset.

Published
2023
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