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6,346 results on '"Wang, Hongyu"'

163. Alterations of pathogen transmission patterns and attenuated immune stimulation might be the cause of increased adult respiratory infections cases in 2023, results from a multi-center study in mainland China

Author

Ai, Jingwen, Wang, Hongyu, Zhang, Haocheng, Song, Jieyu, Zhang, Yi, Lin, Ke, Qu, Lihong, Zhang, Yanliang, Zhang, Shiliang, Xiang, Qiyun, Geng, Jiawei, Jin, Guangxia, Song, Wei, Zhang, Liaoyun, Hu, Xiaoli, Liu, Hongyan, Yuan, Guanmin, Jiang, Ning, Zhou, Yang, Xu, Yuanyuan, Ying, Jun, Wu, Jiqin, Xing, Yajiao, Fang, Kai, Yan, Hui, Chen, Feiying, Xu, Tailin, Wang, Sen, Qian, Zhaohui, and Zhang, Wenhong

Published
2024
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187. Load Forecasting Method for Power Distribution Networks Oriented towards Time Series Simulation with Deep Learning Method

Author

Lu Xiang, wang Hongyu, Zhang Jinpeng, Han Zhongxiu, and Qi Shenglong

Subjects
load forecasting, deep learning, lstm, attention mechanism, 03h10, Mathematics, QA1-939
Abstract

Load forecasting is a critical component of time series simulation in power systems, essential for the reliability and accuracy of simulations. With the integration of renewable energy sources such as photovoltaics, power systems face increasingly complex load forecasting challenges. This paper introduces a deep learning approach that combines Long Short-Term Memory networks (LSTM) and Attention Mechanisms (AM) to enhance the precision and reliability of load forecasting in power distribution networks. Utilizing electric load data from a specific region in China, the LSTM-AM model captures long-term dependencies in time-series data and highlights the impact of critical periods on forecasting accuracy. Experimental results demonstrate that the LSTM-AM model surpasses traditional Back Propagation neural networks, CNNs, and standard LSTM models in terms of prediction precision, affirming the potential application of the proposed method in the field of electric load forecasting. Moreover, the paper introduces a similar day selection strategy to distinguish between weekdays and weekends, reducing RMSE and MAE from 22.6 MW and 15.1 MW to 20.1 MW and 13.9 MW, respectively, thereby further optimizing the accuracy of the model

Published
2024
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188. Recent Colorings And Labelings In Topological Coding

Author

Yao, Bing and Wang, Hongyu

Subjects
Computer Science - Information Theory, Computer Science - Discrete Mathematics, Mathematics - Combinatorics, ACM-class: F.2.2, I.2.7, F.2.2, I.2.7
Abstract

Topological Coding consists of two different kinds of mathematics: topological structure and mathematical relation. The colorings and labelings of graph theory are main techniques in topological coding applied in asymmetric encryption system. Topsnut-gpws (also, colored graphs) have the following advantages: (1) Run fast in communication networks because they are saved in computer by popular matrices rather than pictures. (2) Produce easily text-based (number-based) strings for encrypt files. (3) Diversity of asymmetric ciphers, one public-key corresponds to more private-keys, or more public-keys correspond more private-keys. (4) Irreversibility, Topsnut-gpws can generate quickly text-based (number-based) strings with bytes as long as desired, but these strings can not reconstruct the original Topsnut-gpws. (5) Computational security, since there are many non-polynomial (NP-complete, NP-hard) algorithms in creating Topsnut-gpws. (6) Provable security, since there are many mathematical conjectures (open problems) in graph labelings and graph colorings. We are committed to create more kinds of new Topsnut-gpws to approximate practical applications and antiquantum computation, and try to use algebraic method and Topsnut-gpws to establish graphic group, graphic lattice, graph homomorphism etc., Comment: AMS-LaTeX v1.2, 247 pages with 121 figures. We present new colorings and labelings for topological coding

Published
2021

189. Deep Learning for Depression Recognition with Audiovisual Cues: A Review

Author

He, Lang, Niu, Mingyue, Tiwari, Prayag, Marttinen, Pekka, Su, Rui, Jiang, Jiewei, Guo, Chenguang, Wang, Hongyu, Ding, Songtao, Wang, Zhongmin, Dang, Wei, and Pan, Xiaoying

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

With the acceleration of the pace of work and life, people have to face more and more pressure, which increases the possibility of suffering from depression. However, many patients may fail to get a timely diagnosis due to the serious imbalance in the doctor-patient ratio in the world. Promisingly, physiological and psychological studies have indicated some differences in speech and facial expression between patients with depression and healthy individuals. Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection. To sort out and summarize these works, this review introduces the databases and describes objective markers for automatic depression estimation (ADE). Furthermore, we review the deep learning methods for automatic depression detection to extract the representation of depression from audio and video. Finally, this paper discusses challenges and promising directions related to automatic diagnosing of depression using deep learning technologies.

Published
2021

190. New Intelligent Sensing Terminal System Architecture of Internet of Things

Author

Zhou, Lidan, Li, Jing, Yao, Gang, Wang, Hongyu, Li, Jian, Liu, Siyang, Nie, Yongjie, 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, Dong, Jian, editor, and Zhang, Long, editor

Published
2023
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192. Color Processing System of Art Color Based on Fractional Differential Equation

Author

Wang Hongyu, Cheng Long, and Huang Zheng

Subjects
color repair, fractional differential equation, image processing technology, bridging edge repair, color extraction, fix code settings, 34a08, Mathematics, QA1-939
Abstract

This paper mainly studies a high-precision correction method for warp knitting seamless images. The color factor conversion method is used to transform the gray level of the center point in the frequency domain. Then this paper proposes an image-denoising method based on an average way to improve image quality. The multichannel scalar channel is solved using the gradient domain method, and the fractional differential equations of two images are given. A structure tensor matrix is constructed to describe the geometric properties of images. The regional contrast of the image is calculated by using the characteristics of adjacent layers of pixels. The fusion of multiple images is based on the limitations of object functionality. This can make the texture structure of artistic color pictures more obvious. The experiment of color restoration shows that this method has a good interpretation effect. The color image processing technique has obtained an excellent restoration effect.

Published
2023
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193. PMU abnormal data identification algorithm based on stream clustering

Author

DENG Xiaoyu, WANG Xiangbing, CAO Huazhen, WANG Liuhuo, YAN Hongfeng, and WANG Hongyu

Subjects
phasor measurement unit (pmu), abnormal data, event data, identification framework, information entropy, stream clustering, Applications of electric power, TK4001-4102
Abstract

In order to ensure the accurate application of the data collected by the phasor measurement unit (PMU), it is necessary to eliminate the abnormal data in its measured values. The existing PMU abnormal data identification algorithm has the disadvantages of high algorithm complexity, difficulty in online updating, difficulty in the calibration of multi-source data, and difficulty in application relying on multi-source data. In this paper, an abnormal data identification framework is proposed based on the PMU event data and abnormal data model and the definition of PMU abnormal data identification information entropy. On the basis of the framework, a PMU abnormal data identification algorithm is proposed based on the balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm. The proposed algorithm is implemented, and an algorithm experiment is carried out for the PMU dataset of a substation. The experimental results show that the proposed algorithm has better accuracy and real-time performance than one-class support vector machine (OCSVM) algorithm and gap statistic algorithm.

Published
2023
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