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Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019:A modelling and comparative study

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Infectious Disease Modelling 2024年第3期9卷 816-827页

作者： Xingxing Zhang Liuyang Yang Teng Chen Qing Wang Jin Yang Ting Zhang Jiao Yang Hongqing Zhao Shengjie Lai Luzhao Feng Weizhong Yang School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing100073China State Key Laboratory of Respiratory Health and Multimorbidity China Key Laboratory of Pathogen Infection Prevention and Control(Peking Union Medical College) Ministry of EducationChina National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionChina Department of Management Science and Information System Faculty of Management and EconomicsKunming University of Science and TechnologyKunming650506China Department of Applied Mathematics&Statistics Stony Brook UniversityStony BrookNY11794-3600USA WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonSO171BJUK

Background Influenza is an acute respiratory infectious disease with a significant global disease ***,the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions(NPIs)have introduced uncertainty to the spread of ***,comparative studies on the performance of innovative models and approaches used for influenza prediction are ***,this study aimed to predict the trend of influenza-like illness(ILI)in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive *** The generalized additive model(GAM),deep learning hybrid model based on Gate Recurrent Unit(GRU),and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA—GARCH)model were established to predict the trends of ILI 1-,2-,3-,and 4-week-ahead in Beijing,Tianjin,Shanxi,Hubei,Chongqing,Guangdong,Hainan,and the Hong Kong Special Administrative Region in China,based on sentinel surveillance data from 2011 to *** relevant metrics,namely,Mean Absolute Percentage Error(MAPE),Root Mean Squared Error(RMSE),and R squared,were calculated to evaluate and compare the goodness of fit and robustness of the three *** Considering the MAPE,RMSE,and R squared values,the ARMA—GARCH model performed best,while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in ***,the models’predictive performance declined as the weeks ahead ***,blocked cross-validation indicated that all models were robust to changes in data and had low risks of *** Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid ***,in the future,the ARMA—GARCH model may be used

关键词： Influenza-like illness Influenza Sentinel surveillance China Predicting Modeling

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The Enhancing Effect of the East Asian Subtropical Westerly Jet on Summer Extreme High-Temperature Events over Central-Eastern China Since the Late 1990s

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Advances in Atmospheric Sciences 2025年第3期42卷 453-466页

作者： Chujie GAO Yuyu NIU Gen LI Shanlei SUN Bo LU Chaofan LI Bei XU Jinglong HUANG Xiubao SUN College of Oceanography&Key Laboratory of Marine Hazards Forecasting Ministry of Natural ResourcesHohai UniversityNanjing 210098China Key Laboratory of Meteorological Disaster Ministry of Education(KLME)&Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Tropical Oceanography South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China Laboratory for Climate Studies and CMA-NJU Joint Laboratory for Climate Prediction Studies National Climate CenterChina Meteorological AdministrationBeijing 100081China Xiong’an Institute of Meteorological Artificial Intellegence Xiong'an 071708China Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth and Planetary Sciences University of the Chinese Academy of SciencesBeijing 100049China College of Intelligent Science and Control Engineering Jinling Institute of TechnologyNanjing 211169China

The increase in frequency and intensity of Extreme High-temperature Events(EHEs)over Central-Eastern China(CEC)in recent decades has severely impacted social development and *** observation and reanalysis datasets,this study explores the effect of the East Asian subtropical westerly jet stream(EAJ)on the CEC EHEs for the summers spanning 1979–*** its general relative location to the right side of the upper-level jet stream exit region,CEC would theoretically suffer more EHEs with a stronger and northwardly-shifted EAJ in summer due to the likelihood of abnormal subsidence induced by the ***,such an EAJ–EHE connection has been unstable over the past four decades but has displayed an evident interdecadal *** the late 1990s,the interannual variation of the EAJ was manifested mainly by its meridional displacement in the northeastern part of East Asia;thus,the atmospheric responses were essentially located to the east of CEC,exerting less of an influence on the CEC ***,since the late 1990s,the EAJ variation has featured an intensity change in its center over the northwest portion of the CEC,which has resulted in a westward shift in atmospheric responses to cover the CEC ***,the EAJ could potentially affect the summer CEC EHEs during 2000–*** findings offer support for an in-depth understanding of the formation mechanisms of extreme weather/climate events of this nature and thus provide a scientific reference for seasonal climate predictions.

关键词： summer extreme high-temperature events East Asian subtropical jet atmospheric circulation central-eastern China interdecadal change

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A Binary Sparrow-Hybridized Beluga Whale Optimization Algorithm for Jamming Task Assignment 6

A Binary Sparrow-Hybridized Beluga Whale Optimization Algori...

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6th IEEE International Conference on Electronic information and communication technology, ICEICT 2023

作者： Shen, Chen Gao, Jingpeng Jiang, Zhiye Zhang, Tianran Chen, Geng College of Information and Communication Engineering Harbin Engineering University Harbin150050 China Beijing Institute of Space Long March Vehicle National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics Beijing China Beijing Institute of Electronic System Engineering Beijing100854 China

ISBN: (纸本)9798350399059

In the context of non-cooperative electronic countermeasures, the problem of formulating effective jamming task assignments arises due to insufficient collaborative jamming resources. This paper proposes a binary sparrow- hybridized beluga whale optimization algorithm based on jamming task assignments. The algorithm combines the binary sparrow search algorithm(BSSA) and the binary beluga whale optimization(BBWO), which is set with multi-dimensional constraints in the time, frequency and spatial domains. To achieve the optimal strategy for collaborative interference task assignment, a fitness function is constructed using both radar coverage and the signal-to-jamming ratio received by each radar. When the number of UAVs is 7 and the number of radars is 10, the simulation results show that the optimization speed and accuracy of the proposed algorithm are significantly higher than the BSSA and BBWO. Therefore, the algorithm has good coverage and performance in the field of jamming task assignments. © 2023 IEEE.

关键词： Optimization

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Research on Optimization of Two-Stage Scheduling for Aluminum Extrusion Machine Manufacturing Line

Research on Optimization of Two-Stage Scheduling for Aluminu...

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Chinese Automation Congress (CAC)

作者： Aoran Zhou Lipeng Qiu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In view of the problems of high energy consumption, long inventory time and low production efficiency in the production process of traditional aluminum extrusion machine, this paper establishes an efficient two-stage parallel scheduling optimization model based on the integration of flexible job shop scheduling and assembly sequence planning (IFJSS-ASP). On this basis, an evolutionary algorithm combining strong dominance relationship and simple grouping method (SDREA-SGM) is proposed. The algorithm utilizes a two-stage hybrid chromosome encoding and decoding method to establish a rational mapping from the decision space to the solution space. Furthermore, in order to prevent the model from easily falling into local optima, an adaptive intra-class and inter-class pairing selection strategy has been adopted by the algorithm. This enhances global search and improves local search flexibility. Finally, the feasibility and effectiveness of the algorithm are verified by solving problems of different scales.

关键词： Energy consumption Job shop scheduling Optimization models Aluminum Production Evolutionary computation Encoding Planning Decoding Optimization

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Review of X-ray pulsar spacecraft autonomous navigation

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Chinese Journal of Aeronautics 2023年第10期36卷 44-63页

作者： Yidi WANG Wei ZHENG Shuangnan ZHANG Minyu GE Liansheng LI Kun JIANG Xiaoqian CHEN Xiang ZHANG Shijie ZHENG Fangjun LU College of Aerospace Science and Engineering National University of Defense TechnologyChangsha 410073China Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Chinese Academy of SciencesBeijing 100049China Beijing Institute of Control Engineering Beijing 100080China Beijing Institute of Tracking and Communication Technology Beijing 100091China Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100091China Academy of Military Science Beijing 100091China National Innovation Institute of Defense Technology Academy of Military ScienceBeijing 100091China

This article provides a review on X-ray pulsar-based navigation(XNAV).The review starts with the basic concept of XNAV,and briefly introduces the past,present and future projects concerning *** paper focuses on the advances of the key techniques supporting XNAV,including the navigation pulsar database,the X-ray detection system,and the pulse time of arrival ***,the methods to improve the estimation performance of XNAV are ***,some remarks on the future development of XNAV are provided.

关键词： Estimation of pulse time of arrival Spacecraft autonomous navigation X-ray detection X-ray pulsar-based navigation X-ray pulsar signal processing

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Bayesian Framework for Multi-User Cooperative Radio SLAM 35

Bayesian Framework for Multi-User Cooperative Radio SLAM

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio communications, PIMRC 2024

作者： Que, Hang Yang, Jie Du, Tao Xia, Shuqiang Wen, Chao-Kai Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Zte Corporation Shenzhen518001 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518001 China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan

ISBN: (纸本)9798350362244

The advancement of millimeter-wave communication technology heralds new sensing capabilities. By leveraging channel multipath parameter estimates, we can harness simultaneous localization and mapping (SLAM) for precise user equipment (UE) localization and radio map construction in 6 G communication systems. Particularly in multi-UE scenarios, SLAM empowers base stations to amalgamate the local radio maps of various UEs efficiently. This study introduces a novel Bayesian framework specifically designed for multi-UE SLAM, complemented by a tailored factor graph. We also unveil a two-stage multi-UE SLAM algorithm. Our simulation results reveal that this algorithm substantially enhances radio map construction accuracy by 4 8. 5 % and UE localization accuracy by 13.5 %, outperforming single-UE cases. Moreover, the algorithm demonstrates remarkable adaptability to environmental changes, showcasing its potential for long-term evolution in dynamic settings. © 2024 IEEE.

关键词： Long Term Evolution (LTE)

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Music emotion prediction based on multidimensional feature fusion

Music emotion prediction based on multidimensional feature f...

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information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xuanye Zhou Zhibin Su Hui Ren School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

Audience sentiment analysis is a popular research task in Music information Retrieval (MIR). In this work, we aim to lean the prediction model of fine-grained music emotion for intelligent editing and retrieval. To satisfy the properties of time-series of music, the RNN-based model of BILSTM was selected as the bone. In particular, the traditional features and deep features extracted from the CNN architecture of spectrogram was fused with attention mechanism to enhance the recognition ability for different emotions. The ablation experiment has proved the necessary of the multi-dimensional features. Finally, the contrasting experiments on recent algorithms demonstrate that our proposed method for emotion prediction is effective, which is also suitable for small data sets.

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Fault Diagnosis for Small Samples Based on VMR

Fault Diagnosis for Small Samples Based on VMR

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Chinese Automation Congress (CAC)

作者： Xuejin Wu Yanghu Hu Yali Wu Department of Information and Control Engineering Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China State Key Laboratory of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis.

关键词： Fault diagnosis Training Neural networks Rolling bearings Feature extraction Data models Signal reconstruction Convolutional neural networks Long short term memory Overfitting

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Phase Retrieval by Tensor Total Least Squares

Phase Retrieval by Tensor Total Least Squares

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiani Liu Ce Zhu Yang Chen Xiaolin Huang Yipeng Liu School of Information and Communication Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China Department of Automation The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

Phase retrieval seeks to reconstruct a series of image sequences from measurements that only capture their magnitudes. Current approaches either flatten and stack the image sequences, disregarding their multidimensional structural information, or fail to account for errors within the sensing vectors/tensors. To address these two issues simultaneously, we propose a unified framework for the phase retrieval problem, namely tensor total least squares (TTLS). Specifically, we set up a tensor representation for image sequences and the corresponding measurement model, and for the first time employ the advanced tensor ring network to effectively explore the inherent multidimensional structure for more accurate estimation. Moreover, in addition to the additive noise, the multiplicative errors within the sensing tensor can be also well-corrected, leading to a more robust estimation. Experimental results on both simulated data and real videos demonstrate the superiority of the proposed method.

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Automatic Image Generation of Peking Opera Face using StyleGAN2

Automatic Image Generation of Peking Opera Face using StyleG...

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2022 International Conference on Culture-Oriented Science and technology, CoST 2022

作者： Xin, Xiaoyu Shen, Yinghua Xiong, Rui Lin, Xiahan Yan, Ming Jiang, Wei Communication University of China School of Information and Communication Engineering Beijing China Center for Ethnic and Folk Literature and Art Development Ministry of Culture and Tourism of the P.R.C Beijing China Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Beijing China

ISBN: (数字)9781665462488

ISBN: (纸本)9781665462488

Image generation technology, which is often used in various applications of intelligent image generation, can learn the feature distribution of real images and sample from the distribution to obtain the generated images with high fidelity. This paper focuses on the feature extraction and intelligent generation techniques of Peking opera face with Chinese cultural characteristics. Based on the creation of a Peking opera face dataset, this paper compares the impact of different variants of a Style-based generator architecture for Generative Adversarial Networks (StyleGAN2) and different sizes of datasets on the quality of face generation. The experimental results verify that the synthetic images generated by StyleGAN2 with the addition of the Adaptive Discriminator Augmentation (ADA) module are visually better and have good local randomness when the dataset is small and unbalanced in distribution. © 2022 IEEE.

关键词： Generative adversarial networks

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