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Parameter identifiability of a within-host SARS-CoV-2 epidemic model

Infectious Disease Modelling 2024年第3期9卷 975-994页

作者： Junyuan Yang Sijin Wu Xuezhi Li Xiaoyan Wang Xue-Song Zhang Lu Hou Complex Systems Research Center Shanxi UniversityTaiyuan030006China Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi UniversityTaiyuan030006China School of Mathematics and Science Henan Normal UniversityXinxiang453000China School of Information Shanxi University of Finance and EconomicsTaiyuan030006China Agriculture and Animal Husbandry Technology Promotion Center of Xingan League Xingan League137400China

Parameter identification involves the estimation of undisclosed parameters within a system based on observed data and mathematical *** this investigation,we employ DAISY to meticulously examine the structural identifiability of parameters of a within-host SARS-CoV-2 epidemic model,taking into account an array of observable ***,Monte Carlo simulations are performed to offer a comprehensive practical analysis of model ***,sensitivity analysis is employed to ascertain that decreasing the replication rate of the SARS-CoV-2 virus and curbing the infectious period are the most efficacious measures in alleviating the dissemination of COVID-19 amongst hosts.

关键词： Structural identifiability Practical identifiability Sensitivity analysis The basic reproduction number

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An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data

SSRN

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SSRN 2022年

作者： Khuat, Thanh Tung Gabrys, Bogdan Complex Adaptive Systems Laboratory Data Science Institute University of Technology SydneyNSW Australia

General fuzzy min-max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms is the inability to handle and learn from the mixed-attribute data. The existing approaches are not suitable for online learning algorithms working in the dynamically changing environments without ability to retrain or access full historical data, which are usually required for many real world applications. This paper proposes an extended online learning algorithm for the GFMMNN. The proposed method can handle the datasets with both continuous and categorical features. It uses the change in the entropy values of categorical features of the samples contained in a hyperbox to determine if the current hyperbox can be expanded to include the categorical values of a new training instance. An extended architecture of the original GFMMNN and its new membership function are introduced for mixed-attribute data. Important mathematical properties of the proposed learning algorithms are also presented and proved in this paper. The extensive experiments confirmed superior and stable classification performance of the proposed approach in comparison to other relevant learning algorithms forthe GFMM model. © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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A semi-supervised reject inference framework with hierarchical heterogeneous networks for credit scoring

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International Journal of Forecasting 2025年第3期41卷 920-939页

作者： Chen, Liao Jia, Ning Jiao, Zhixian Zhao, Hongke Cui, Runbang Wang, Huimin School of Management and E-Business Zhejiang Gongshang University Hangzhou 310018 China College of Management and Economics Tianjin University Tianjin 300072 China Laboratory of Computation and Analytics of Complex Management Systems (CACMS) Tianjin University Tianjin 300072 China Beijing Fantastic Technology Co. Ltd Beijing 100080 China State Key Laboratory of Hydrology Water Resource and Hydraulic Engineering Hohai University Nanjing 210098 China Management Science Institute Hohai University Nanjing 210098 China

Credit scoring is a popular tool for loan assessment, i.e., deciding whether to accept or reject a loan application. Traditional research into learning for credit scoring has only applied historically accepted samples without rejected applicants whose true repayment performance is absent, thereby causing both sample selection bias and wasting data. Some methods have been proposed for inferring rejected samples but they are still affected by several open problems, especially for medium- and long-term loan applications with a higher rejection rate. In particular, the heterogeneous relationships between accepted and rejected applications have not been well studied. Moreover, the complex repayment behaviors resulting from long repayment terms may lead to poor learning performance. Thus, we propose a reject inference framework with Semi-supervised Hierarchical Heterogeneous Network (S2HN) for credit scoring. We introduce a hierarchical heterogeneous network for revealing the complex connections between accepted and rejected applications, and use prospective heterogeneous repayment patterns as auxiliary information through clustering and a two-layer prediction architecture. Extensive experiments conducted based on real-world data sets demonstrated the effectiveness of our proposed method. © 2024 International Institute of Forecasters

关键词： Credit scoring Deep learning Graph neural network Heterogeneous network Reject inference

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Control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks ⁎

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IFAC-PapersOnLine 2024年第10期58卷 176-181页

作者： Yi Li Shuangshuang Han Yongqiang Bai Fei-Yue Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Key Laboratory of Ocean Observation Technology MNR Tianjin China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Beijing Huairou Academy of Parallel Sensing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

关键词： VANETs Social relation strength Image data transmission MMSE detection Intelligent transportation Social networking

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Research on Radar Active Deception Jamming Identification Method Based on RESNET and Bispectrum Features 2

Research on Radar Active Deception Jamming Identification Me...

引用

2nd International Conference on Computer Engineering and Application, ICCEA 2021

作者： Wang, Kunteng Dong, Zeyu Wan, Tao Jiang, Kaili Xiong, Wanan Fang, Xueli University of Electronic Science Technology of China School of Electronic Science and Engineering Chengdu China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Key Laboratory of Complex Aviation Simulation System Beijing China

ISBN: (纸本)9781665426169

In the complex electromagnetic environment of modern electronic warfare, active deception jamming poses a serious threat to radar. This paper proposes the identification of active deception jamming based on residual neural network (RESNET), and studies the bispectral characteristics of the signal and the method of identifying the active deception jamming of RESNET. This algorithm firstly extracts the bispectral features of the jamming signal, and uses the bispectral diagonal slice as the input of RSNET to realize the recognition of active deception jamming. simulation results show that the algorithm has high recognition accuracy, not only has good anti-noise performance, but also has excellent robustness under low JSR. © 2021 IEEE.

关键词： Radar

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Adaptive radar detection of subspace-based distributed target in power heterogeneous clutter

arXiv

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arXiv 2024年

作者： Xiao, Daipeng Liu, Weijian Liu, Jun Dai, Lingyan Fang, Xueli Ge, Jianjun Wuhan Electronic Information Institute Wuhan430019 China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Key Laboratory of Complex Aviation Simulation System Beijing100076 China Information Science Academy China Electronics Technology Group Corporation Beijing100041 China

This paper investigates the problem of adaptive detection of distributed targets in power heterogeneous clutter. In the considered scenario, all the data share the identical structure of clutter covariance matrix, but with varying and unknown power mismatches. To address this problem, we iteratively estimate all the unknowns, including the coordinate matrix of the target, the clutter covariance matrix, and the corresponding power mismatches, and propose three detectors based on the generalized likelihood ratio test (GLRT), Rao and the Wald tests. The results from simulated and real data both illustrate that the detectors based on GLRT and Rao test have higher probabilities of detection (PDs) than the existing competitors. Among them, the Rao test-based detector exhibits the best overall detection performance. We also analyze the impact of the target extended dimensions, the signal subspace dimensions, and the number of training samples on the detection performance. Furthermore, simulation experiments also demonstrate that the proposed detectors have a constant false alarm rate (CFAR) property for the structure of clutter covariance matrix. © 2024, CC BY.

关键词： Radar clutter

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Parameter Inversion of High-Dimensional Chaotic systems Using Neural Ordinary Differential Equations

Parameter Inversion of High-Dimensional Chaotic Systems Usin...

引用

Data Driven Control and Learning systems (DDCLS)

作者： Hanlin Bian Wei Zhu Zhang Chen L. Jingsui Chao Pei Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

High-dimensional chaotic systems possess complex structural characteristics, which have potential research value in fields like encrypted communication and chaos control. The inverse problem of parameters in high-dimensional chaotic systems is of significant importance for a profound understanding of the dynamic properties of such systems. This article takes a departure from neural ordinary differential equations and embeds the structural dynamics of the dynamical systems to the parameter inversion of high-dimensional chaotic systems. Numerical experiments on different high-dimensional chaotic systems indicate that neural ordinary differential equations perform exceptionally well in solving these inversion problems, with low errors and strong robustness.

关键词： Resistance Learning systems Chaotic communication Inverse problems Noise Ordinary differential equations Robustness

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Designing Consensus-Based Distributed Filtering over Directed Graphs

arXiv

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arXiv 2024年

作者： Lyu, Xiaoxu Wen, Guanghui Lv, Yuezu Duan, Zhisheng Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Systems Science School of Mathematics Southeast University Nanjing211189 China MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper proposes a novel consensus-on-only-measurement distributed filter over directed graphs under the collectively observability condition. First, the distributed filter structure is designed with an augmented leader-following measurement fusion strategy. Subsequently, two parameter design methods are presented, and the consensus gain parameter is devised utilizing local information exclusively rather than global information. Additionally, the lower bound of the fusion step is derived to guarantee a uniformly upper bound of the estimation error covariance. Moreover, the lower bounds of the convergence rates of the steady-state performance gap between the proposed algorithm and the centralized filter are provided with the fusion step approaching infinity. The analysis demonstrates that the convergence rate is, at a minimum, as rapid as exponential convergence under the spectral norm condition of the communication graph. The transient performance is also analyzed with the fusion step tending to infinity. The inherent trade-off between the communication cost and the filtering performance is revealed from the analysis of the steady-state performance and the transient performance. Finally, the theoretical results are substantiated through the validation of two simulation examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods

Detection of Detonators in Baggage Using Deep CNN Architectu...

引用

International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Oulhissane Lynda Merah Mostefa Simona Moldovanu Luminita Moraru Laboratory of Signals and Systems LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Mostaganem Algeria MSlab Department of Computer Science and Information Technology Modelling & Simulation Laboratory Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania MSlab Department of Chemistry Physics & Environment Modelling & Simulation Laboratory Faculty of Sciences and Environment Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL

关键词： Wavelet transforms Radiography Visualization Gray-scale Inspection Feature extraction Libraries

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