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Bingwen Wang

dblp:40/4013 · DBLP profile ↗
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8ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 6 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › activation function
activation function design
0.012004
A fast learning algorithm of neural network with tunable activation function · Sci. China Ser. F Inf. Sci. 2004
Machine learning › Deep learning architectures and training › activation function
trainable activation function
0.012004
A fast learning algorithm of neural network with tunable activation function · Sci. China Ser. F Inf. Sci. 2004
YearPublicationVenuePosition
2026 Improving adversarial transferability via semantic-style joint expectation perturbations
Bingwen Wang, Kang Deng, Anjie Peng
Pattern Recognit.2
2022 Multi-features guidance network for partial-to-partial point cloud registration
Wen Kang, Bingwen Wang, Qianhao Ning
Neural Comput. Appl.5
2016 A Frequency-Hopping Subspace-Based Optimization Method for Reconstruction of 2-D Large Uniaxial Anisotropic Scatterers With TE Illumination
abstract
Due to the nonlinear property of large uniaxial anisotropic scatterers, many iterative optimization methods have a high risk of being trapped in local minima. In this paper, a frequency-hopping subspace-based optimization method (SOM) is proposed to reconstruct the relative permittivity distribution of 2-D large uniaxial anisotropic scatterers with transverse electrical (TE) illumination. This hybrid method utilizes the results obtained at lower frequency to provide good initial guesses for higher frequency reconstruction, which reduces the occurrence of local minima for the inversion at the higher frequency. For lower frequency, it can only obtain coarse resolution image although it is unable to show the details of the scatterers. However, this coarse image provides a priori information for the reconstruction at higher frequencies to get finer resolution. Numerical examples demonstrate that the proposed hybrid method can effectively rebuild large uniaxial anisotropic scatterers (six wavelengths) with higher stability compared with conventional SOM that uses only single-frequency data.
Yulang Liu, Zhiqin Zhao, Yaohui Yang, Bingwen Wang, Xiaozhang Zhu, Zaiping Nie, Qing Huo Liu
IEEE Trans. Geosci. Remote. Sens.4
2009 RTKPS: A Key Pre-distribution Scheme Based on Rooted-Tree in Wireless Sensor and Actor Network
Zhicheng Dai, Zhi Li 0027, Bingwen Wang, Qiang Tang 0004
ISNN (3)3
2009 Back Propagation Neural Network Based Lifetime Analysis of Wireless Sensor Network
Bingwen Wang, Xiaoya Hu
ISNN (3)2
2009 Design and Implementation of the Structure Health Monitoring System for Bridge Based on Wireless Sensor Network
An Yin, Bingwen Wang, Xiaoya Hu
ISNN (3)2
2004 A fast learning algorithm of neural network with tunable activation function
Bingwen Wang
Sci. China Ser. F Inf. Sci.2
2004 A New Multi-output Neural Model with Tunable Activation Function and its Applications
Bingwen Wang, Fangxin Chen, Liang Cheng 0001
Neural Process. Lett.2