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377 results on '"Wang Wenzhuo"'

1. A reactive voltage control strategy for distributed PV based on cluster segmentation

Author

WANG Wenzhuo, XIE Ding, XIE Zuibing, XU Haichao, FAN Jiajie, LIU Caihua, and TIAN Xinze

Subjects
distributed pv, cluster segmentation, reactive voltage control, improved modularity index, cluster voltage control, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

In response to the voltage over-limit in distributed photovoltaic （PV） distribution networks， a reactive voltage control strategy considering cluster segmentation of distributed PV is proposed. Firstly， a modularity index based on reactive voltage sensitivity is introduced； taking account of the reactive balance and coupling within clusters， an improved modularity index is obtained and used for cluster segmentation. Then， a strategy for reactive voltage control within over-limit clusters and a coordinated control strategy between clusters are proposed. When overvoltage clusters cannot restore voltage to a safe level internally， the strategy involves selecting lead nodes from safe clusters with adjustable reactive power capacity to allocate the remaining adjustable reactive power capacity to the overvoltage clusters. Finally， simulations are conducted using an IEEE 33-bus distribution network to validate the effectiveness of the proposed strategy.

Published
2024
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2. Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting

Author

Ruan, Weilin, Wang, Wenzhuo, Zhong, Siru, Chen, Wei, Liu, Li, and Liang, Yuxuan

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

Predicting spatio-temporal traffic flow presents significant challenges due to complex interactions between spatial and temporal factors. Existing approaches often address these dimensions in isolation, neglecting their critical interdependencies. In this paper, we introduce the Spatio-Temporal Unitized Model (STUM), a unified framework designed to capture both spatial and temporal dependencies while addressing spatio-temporal heterogeneity through techniques such as distribution alignment and feature fusion. It also ensures both predictive accuracy and computational efficiency. Central to STUM is the Adaptive Spatio-temporal Unitized Cell (ASTUC), which utilizes low-rank matrices to seamlessly store, update, and interact with space, time, as well as their correlations. Our framework is also modular, allowing it to integrate with various spatio-temporal graph neural networks through components such as backbone models, feature extractors, residual fusion blocks, and predictive modules to collectively enhance forecasting outcomes. Experimental results across multiple real-world datasets demonstrate that STUM consistently improves prediction performance with minimal computational cost. These findings are further supported by hyperparameter optimization, pre-training analysis, and result visualization. We provide our source code for reproducibility at https://anonymous.4open.science/r/STUM-E4F0.

Published
2024

12. Combating Illegal Wildlife Trade through Big Data Monitoring and Management

Author

Wang, Wenzhuo, He, Jianing, Liu, Shuo, Dou, Runliang, Editor-in-Chief, Liu, Jing, Editor-in-Chief, Khasawneh, Mohammad T., Editor-in-Chief, Balas, Valentina Emilia, Series Editor, Bhowmik, Debashish, Series Editor, Khan, Khalil, Series Editor, Masehian, Ellips, Series Editor, Mohammadi-Ivatloo, Behnam, Series Editor, Nayyar, Anand, Series Editor, Pamucar, Dragan, Series Editor, Shu, Dewu, Series Editor, Haldorai, Anandakumar, editor, Singh, Dilbag, editor, Kumar, Anil, editor, Talpur, Mir Sajjad Hussain, editor, and Djeddi, Chawki, editor

Published
2024
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21. Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution

Author

Tao, Guangpin, Ji, Xiaozhong, Wang, Wenzhuo, Chen, Shuo, Lin, Chuming, Cao, Yun, Lu, Tong, Luo, Donghao, and Tai, Ying

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep-learning based Super-Resolution (SR) methods have exhibited promising performance under non-blind setting where blur kernel is known. However, blur kernels of Low-Resolution (LR) images in different practical applications are usually unknown. It may lead to significant performance drop when degradation process of training images deviates from that of real images. In this paper, we propose a novel blind SR framework to super-resolve LR images degraded by arbitrary blur kernel with accurate kernel estimation in frequency domain. To our best knowledge, this is the first deep learning method which conducts blur kernel estimation in frequency domain. Specifically, we first demonstrate that feature representation in frequency domain is more conducive for blur kernel reconstruction than in spatial domain. Next, we present a Spectrum-to-Kernel (S$2$K) network to estimate general blur kernels in diverse forms. We use a Conditional GAN (CGAN) combined with SR-oriented optimization target to learn the end-to-end translation from degraded images' spectra to unknown kernels. Extensive experiments on both synthetic and real-world images demonstrate that our proposed method sufficiently reduces blur kernel estimation error, thus enables the off-the-shelf non-blind SR methods to work under blind setting effectively, and achieves superior performance over state-of-the-art blind SR methods, averagely by 1.39dB, 0.48dB on commom blind SR setting (with Gaussian kernels) for scales $2\times$ and $4\times$, respectively., Comment: Accepted to NeurIPS 2021

Published
2021

23. Coordinated Control of Commutation Failure Prevention Under UHVDC Multi-infeed System

Author

Wang, Zhiwei, Tan, Yangchen, Liu, Xin, Wang, Wenzhuo, Li, Zheng, Wei, Zhaoyang, Su, Biao, Wang, Jiahao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, 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, 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, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, 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, Tan, Kay Chen, Series Editor, Dong, Xuzhu, editor, Yang, Qingxin, editor, and Ma, Weiming, editor

Published
2023
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25. Model Construction and Empirical Study of China's Automobile Industry and Entrepreneur Confidence Index

Author

Li, Xudong, Zuo, Peiwen, Wang, Wenzhuo, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Fernández-Viagas, Víctor, editor, Kandel, Bijay Kumar, editor, Valmohammadi, Changiz, editor, and Mallick, Hrushikesh, editor

Published
2023
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28. HSPA12A was identified as a key driver in colorectal cancer GWAS loci 10q26.12 and modulated by an enhancer–promoter interaction

Author

Lu, Zequn, Fan, Linyun, Zhang, Fuwei, Huang, Chaoqun, Cai, Yimin, Chen, Can, Li, Gaoyuan, Zhang, Ming, Huang, Jinyu, Ning, Caibo, Li, Yanmin, Wang, Wenzhuo, Geng, Hui, Liu, Yizhuo, Chen, Shuoni, Li, Hanting, Yang, Shuhui, Zhang, Heng, Tian, Wen, Ye, Tianrun, Liu, Jiuyang, Yang, Xiaojun, Xu, Bin, Zhu, Ying, Zhong, Rong, Li, Heng, Tian, Jianbo, Li, Bin, and Miao, Xiaoping

Published
2023
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29. Genome-wide association analysis of left ventricular imaging-derived phenotypes identifies 72 risk loci and yields genetic insights into hypertrophic cardiomyopathy

Author

Ning, Caibo, Fan, Linyun, Jin, Meng, Wang, Wenji, Hu, Zhiqiang, Cai, Yimin, Chen, Liangkai, Lu, Zequn, Zhang, Ming, Chen, Can, Li, Yanmin, Zhang, Fuwei, Wang, Wenzhuo, Liu, Yizhuo, Chen, Shuoni, Jiang, Yuan, He, Chunyi, Wang, Zhuo, Chen, Xu, Li, Hanting, Li, Gaoyuan, Ma, Qianying, Geng, Hui, Tian, Wen, Zhang, Heng, Liu, Bo, Xia, Qing, Yang, Xiaojun, Liu, Zhongchun, Li, Bin, Zhu, Ying, Li, Xiangpan, Zhang, Shaoting, Tian, Jianbo, and Miao, Xiaoping

Published
2023
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30. Genome-wide enhancer-gene regulatory maps link causal variants to target genes underlying human cancer risk

Author

Ying, Pingting, Chen, Can, Lu, Zequn, Chen, Shuoni, Zhang, Ming, Cai, Yimin, Zhang, Fuwei, Huang, Jinyu, Fan, Linyun, Ning, Caibo, Li, Yanmin, Wang, Wenzhuo, Geng, Hui, Liu, Yizhuo, Tian, Wen, Yang, Zhiyong, Liu, Jiuyang, Huang, Chaoqun, Yang, Xiaojun, Xu, Bin, Li, Heng, Zhu, Xu, Li, Ni, Li, Bin, Wei, Yongchang, Zhu, Ying, Tian, Jianbo, and Miao, Xiaoping

Published
2023
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32. Genetic Control of Alternative Splicing and its Distinct Role in Colorectal Cancer Mechanisms

Author

Zhang, Ming, Chen, Can, Lu, Zequn, Cai, Yimin, Li, Yanmin, Zhang, Fuwei, Liu, Yizhuo, Chen, Shuoni, Zhang, Heng, Yang, Shuhui, Gen, Hui, Jiang, Yuan, Ning, Caibo, Huang, Jinyu, Wang, Wenzhuo, Fan, Linyun, Zhang, Yi, Jin, Meng, Han, Jinxin, Xiong, Zhen, Cai, Ming, Liu, Jiuyang, Huang, Chaoqun, Yang, Xiaojun, Xu, Bin, Li, Heng, Li, Bin, Zhu, Xu, Wei, Yongchang, Zhu, Ying, Tian, Jianbo, and Miao, Xiaoping

Published
2023
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44. C-type two-thermocouple sensor design between 1000 and 1700 °C.

Author

Guo, Yangkai, Zhang, Zhijie, Li, Yanfeng, and Wang, Wenzhuo

Subjects
COST functions, STANDARD deviations, TEMPERATURE measurements, GOODNESS-of-fit tests, SYSTEM identification
Abstract

At the present stage of transient ultra-high-temperature energy release of boron-containing warm-pressure explosives, single thermocouples are often used for multi-point measurements in the process of their temperature field changes, and the results of their temperature field reconstruction are not satisfactory due to the limited consistency of the thermocouples. Aiming at the above-mentioned problems, a C-type two-thermocouple suitable for transient temperature measurement in high-temperature environments is designed; the system characterization of the two-thermocouple is carried out by using the blind system identification method of the inter-relationships; the identification process is evaluated by a new cost function; and the optimal solution on the new cost function is realized by using the gradient descent method. The temperature reconstruction of the two-thermocouple output excited by the simulated heat source is carried out by using the auto-regressive with extra inputs model, and the feasibility of the reconstruction results is verified. In the experimental part, a thermocouple dynamic characteristic calibration system based on a high-temperature furnace is constructed and experimental validation is carried out in the high-temperature furnace to compare the effects of different exposure lengths and different wire diameters on the output of the two-thermocouple, and as a result, the outputs are corrected and analyzed. The results show that two-thermocouple methods with different combinations of wire diameters are better for temperature measurement, with a reconstructed root mean squared error of 0.0162 and a goodness of fit of 89.13%. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Degradation of Natural Undaria pinnatifida into Unsaturated Guluronic Acid Oligosaccharides by a Single Alginate Lyase.

Author

Wang, Hui, Wen, Jiaqi, Ablimit, Nuraliya, Deng, Kun, Wang, Wenzhuo, and Jiang, Wei

Abstract

Here, we report on a bifunctional alginate lyase (Vnalg7) expressed in Pichia pastoris, which can degrade natural Undaria pinnatifida into unsaturated guluronic acid di- and trisaccharide without pretreatment. The enzyme activity of Vnalg7 (3620.00 U/mL-culture) was 15.81-fold higher than that of the original alg (228.90 U/mL-culture), following engineering modification. The degradation rate reached 52.75%, and reducing sugar reached 30.30 mg/mL after combining Vnalg7 (200.00 U/mL-culture) and 14% (w/v) U. pinnatifida for 6 h. Analysis of the action mode indicated that Vnalg7 could degrade many substrates to produce a variety of unsaturated alginate oligosaccharides (AOSs), and the minimal substrate was tetrasaccharide. Site-directed mutagenesis showed that Glu238, Glu241, Glu312, Arg236, His307, Lys414, and Tyr418 are essential catalytic sites, while Glu334, Glu344, and Asp311 play auxiliary roles. Mechanism analysis revealed the enzymatic degradation pattern of Vnalg7, which mainly recognizes and attacks the third glycosidic linkage from the reducing end of oligosaccharide substrate. Our findings provide a novel alginate lyase tool and a sustainable and commercial production strategy for value-added biomolecules using seaweeds. [ABSTRACT FROM AUTHOR]

Published
2024
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