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379 results on '"Wang, Zewen"'

1. Carleman estimates for space semi-discrete approximations of one-dimensional stochastic parabolic equation and its applications

Author

Wu, Bin, Wang, Ying, and Wang, Zewen

Subjects
Mathematics - Probability, Mathematics - Analysis of PDEs
Abstract

In this paper, we study discrete Carleman estimates for space semi-discrete approximations of one-dimensional stochastic parabolic equation. As applications of these discrete Carleman estimates, we apply them to study two inverse problems for the spatial semi-discrete stochastic parabolic equations, including a discrete inverse random source problem and a discrete Cauchy problem. We firstly establish two Carleman estimates for a one-dimensional semi-discrete stochastic parabolic equation, one for homogeneous boundary and the other for non-homogeneous boundary. Then we apply these two estimates separately to derive two stability results. The first one is the Lipschitz stability for the discrete inverse random source problem. The second one is the H\"{o}lder stability for the discrete Cauchy problem.

Published
2024

2. Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition

Author

Luo, Bingjun, Wang, Zewen, Wang, Jinpeng, Zhu, Junjie, Zhao, Xibin, and Gao, Yue

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction
Abstract

Illumination variation has been a long-term challenge in real-world facial expression recognition(FER). Under uncontrolled or non-visible light conditions, Near-infrared (NIR) can provide a simple and alternative solution to obtain high-quality images and supplement the geometric and texture details that are missing in the visible domain. Due to the lack of existing large-scale NIR facial expression datasets, directly extending VIS FER methods to the NIR spectrum may be ineffective. Additionally, previous heterogeneous image synthesis methods are restricted by low controllability without prior task knowledge. To tackle these issues, we present the first approach, called for NIR-FER Stochastic Differential Equations (NFER-SDE), that transforms face expression appearance between heterogeneous modalities to the overfitting problem on small-scale NIR data. NFER-SDE is able to take the whole VIS source image as input and, together with domain-specific knowledge, guide the preservation of modality-invariant information in the high-frequency content of the image. Extensive experiments and ablation studies show that NFER-SDE significantly improves the performance of NIR FER and achieves state-of-the-art results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.

Published
2023

15. Whole-exome sequencing explored mechanism of selpercatinib resistance in RET-rearranged lung adenocarcinoma transformation into small-cell lung cancer: a case report

Author

Yan Peng, Zhu Zheng, Wang Zewen, Liu Yanan, Zhang Mingyan, and Sun Meili

Subjects
NSCLC, RET-rearranged, Whole-exome sequencing, Small cell transformation, Diseases of the respiratory system, RC705-779
Abstract

Abstract Small cell transformation was one mechanism by which EGFR-mutation NSCLC acquired resistance after tyrosine kinase inhibitors (TKIs) treatment. A few reports of small cell transformation occurred in other oncogene-driven lung cancers. We found the first case of transformation of a RET-rearranged lung adenocarcinoma to SCLC after selpercatinib, a novel highly selective RET TKIs. Whole-exome sequencing (WES) was used to explore alteration in gene expression in tumor tissue at initial diagnosis and after transformation into small cell carcinoma. We found that transformed into SCLC tumor tissue had inactivation of RB1 and TP53, with RET fusion was still present. In addition, the APOBEC family of cytidine deaminases appeared amplification. Although RET rearrangement still existed, using another RET TKIs was ineffective, and etoposide plus platinum might be an effective rescue treatment.

Published
2023
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23. Prediction of circRNA-Binding Protein Site Based on Hybrid Neural Networks and Recurrent Forests Method

Author

Wang, Zewen, Meng, Qingfang, Zhang, Qiang, Zhang, Jiahao, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Premaratne, Prashan, editor, Jin, Baohua, editor, Qu, Boyang, editor, Jo, Kang-Hyun, editor, and Hussain, Abir, editor

Published
2023
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24. Seizure Prediction Based on Multidimensional EEG Spatial Matrix and Residual Network Structure

Author

Zhang, Jiahao, Meng, Qingfang, Wang, Zewen, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Premaratne, Prashan, editor, Jin, Baohua, editor, Qu, Boyang, editor, Jo, Kang-Hyun, editor, and Hussain, Abir, editor

Published
2023
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25. Analysis of Geometric Characteristics of Three-Terminal Shaft Loosely Coupled Transformer Based on LCC-S Compensation

Author

Kang, Shijia, Wang, Zewen, Zhang, Zijia, Li, Yansong, Liu, Jun, 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, Ma, Chengbin, editor, Zhang, Yiming, editor, Li, Siqi, editor, Zhao, Lei, editor, Liu, Ming, editor, and Zhang, Pengcheng, editor

Published
2023
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30. Carleman estimates for a stochastic degenerate parabolic equation and applications to null controllability and an inverse random source problem

Author

Wu, Bin, Chen, Qun, and Wang, Zewen

Subjects
Mathematics - Optimization and Control
Abstract

In this paper, we establish two Carleman estimates for a stochastic degenerate parabolic equation. The first one is for the backward stochastic degenerate parabolic equation with singular weight function. Combining this Carleman estimate and an approximate argument, we prove the null controllability of the forward stochastic degenerate parabolic equation with the gradient term. The second one is for the forward stochastic degenerate parabolic equation with regular weighted function, based on which we obtain the Lipschitz stability for an inverse problem of determining a random source depending only on time in the forward stochastic degenerate parabolic equation.

Published
2019
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32. Haplotype-resolved T2T genome assemblies and pangenome graph of pear reveal diverse patterns of allele-specific expression and genomic basis of fruit quality traits

Author

Li, Qionghou, primary, Qiao, Xin, additional, Li, Lanqing, additional, Gu, Chao, additional, Yin, Hao, additional, Qi, Kaijie, additional, Xie, Zhihua, additional, Yang, Sheng, additional, Zhao, Qifeng, additional, Wang, Zewen, additional, Yang, Yuhang, additional, Pan, Jiahui, additional, Li, Hongxiang, additional, Wang, Jie, additional, Wang, Chao, additional, Rieseberg, Loren H., additional, Zhang, Shaoling, additional, and Tao, Shutian, additional

Published
2024
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36. Side-Scan Sonar Image Generator Based on Diffusion Models for Autonomous Underwater Vehicles.

Author

Zhang, Feihu, Hou, Xujia, Wang, Zewen, Cheng, Chensheng, and Tan, Tingfeng

Subjects
OBJECT recognition (Computer vision), ACOUSTIC imaging, SONAR imaging, ACOUSTIC signal processing, IMAGE intensifiers
Abstract

In the field of underwater perception and detection, side-scan sonar (SSS) plays an indispensable role. However, the imaging mechanism of SSS results in slow information acquisition and high complexity, significantly hindering the advancement of downstream data-driven applications. To address this challenge, we designed an SSS image generator based on diffusion models. We developed a data collection system based on Autonomous Underwater Vehicles (AUVs) to achieve stable and rich data collection. For the process of converting acoustic signals into image signals, we established an image compensation method based on nonlinear gain enhancement to ensure the reliability of remote signals. On this basis, we developed the first controllable category SSS image generation algorithm, which can generate specified data for five categories, demonstrating outstanding performance in terms of the Fréchet Inception Distance (FID) and the Inception Score (IS). We further evaluated our image generator in the task of SSS object detection, and our cross-validation experiments showed that the generated images contributed to an average accuracy improvement of approximately 10% in object detection. The experimental results validate the effectiveness of the proposed SSS image generator in generating highly similar sonar images and enhancing detection accuracy, effectively addressing the issue of data scarcity. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Retinoic Acid-Poly(lactic) Acid Composite for Biocompatible Optoelectronic Synapses with Flexible Curvature Detection and Biomimetic Imprinting Capabilities

Author

Wang, Mingqiang, primary, Wang, Jie, additional, Li, Lei, additional, Liu, Yanxin, additional, Peng, Zehui, additional, Duan, Xinqing, additional, Feng, Xibei, additional, Wang, Zewen, additional, Wang, Shidong, additional, Wang, Mingge, additional, Jin, Yufeng, additional, Hsieh, Dar-Jen, additional, and Chang, Kuan-Chang, additional

Published
2024
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42. Anionic Ionomer: Released Surface-Immobilized Cations and an Established Hydrophobic Microenvironment for Efficient and Durable CO2-to-Ethylene Electrosynthesis at High Current over One Month

Author

Fang, Mingwei, Miao, Xiang, Huang, Zihao, Wang, Meiling, Feng, Xiaochen, Wang, Zewen, Zhu, Ying, Dai, Liming, and Jiang, Lei

Abstract

Electrosynthesis of multicarbon products, such as C2H4, from CO2reduction on copper (Cu) catalysts holds promise for achieving carbon neutrality. However, maintaining a steady high current-level C2H4electrosynthesis still encounters challenges, arising from unstable alkalinity and carbonate precipitation caused by undesired ion migration at the cathode under a repulsive electric field. To address these issues, we propose a universal “charge release” concept by incorporating tiny amounts of an oppositely charged anionic ionomer (e.g., perfluorinated sulfonic acid, PFSA) into a cationic covalent organic framework on the Cu surface (cCOF/PFSA). This strategy effectively releases the hidden positive charge within the cCOF, enhancing surface immobilization of cations to impede both outward migration of generated OH–and inward migration of cations, inhibiting carbonate precipitation and creating a strong alkaline microenvironment. Meanwhile, the ionomer’s hydrophobic chains create a hydrophobic environment within the cCOF, facilitating efficient gas transport. In situ characterizations and theoretical calculations demonstrate that the cCOF/PFSA catalyst establishes a hydrophobic strong alkaline microenvironment, optimizing the adsorption strength and configuration of *CO intermediates to promote the C2H4formation. The optimized catalyst achieves a 70.5% Faradaic efficiency for C2H4with a partial current density over 470 mA cm–2. Notably, it delivers a high single-pass carbon efficiency of 96.5% for CO2RR and sustains an exceptional stability over 760 h. When implemented in a large-area MEA electrolyzer and a 5-cell MEA stack, the system achieves an industrial current of 15 A and continuous C2H4production exceeding 19 mL min–1, marking a significant step toward industrial feasibility in CO2RR-to-C2H4conversion.

Published
2024
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43. Sideband Harmonic-Based Talkative Power Conversion

Author

Wang, Zewen, Zhou, Dehong, Liu, Xin, Shen, Zewei, and Zou, Jianxiao

Abstract

Talkative power conversion (TPC) is a promising solution to modern power system communication due to its advanced features of reduced communication infrastructure and low volume. However, the majority of TPC in three-phase inverters is realized by superimposing the frequency-hopping sinusoidal reference signals, which adversely affects the power quality and stability from the perspective of the power system. In view of this, this article proposed a sideband harmonic-based TPC scheme for three-phase inverters without extra harmonics injection. In the proposed scheme, the signal is modulated into the carrier-based pulsewidth modulation by binary phase-shift keying modulation and is transmitted over power lines via sideband harmonics. Sideband harmonics with different carrier phases are analyzed by double Fourier series (DFS) and modeled under dq frame for efficient demodulation. The proposed strategy takes advantage of the existing sideband harmonics to achieve TPC in three-phase inverters, which avoids introducing extra harmonics and has less impact on the power quality. As a result, the given solution not only reduces system costs and volume but also realizes a power quality-friendly signal transmission. Finally, the feasibility of the proposed TPC method is verified by the experimental platform.

Published
2024
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46. Underwater Mapping and Optimization Based on Multibeam Echo Sounders.

Author

Zhang, Feihu, Tan, Tingfeng, Hou, Xujia, Zhao, Liang, Cao, Chun, and Wang, Zewen

Subjects
INFORMATION storage & retrieval systems, UNDERWATER exploration, POINT cloud, ECHO sounders, KALMAN filtering
Abstract

Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration. [ABSTRACT FROM AUTHOR]

Published
2024
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