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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Wang, Liyuqi Tang, Mengjiao Rong, Yao Ni, Meixin Li, Fan Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis Kunming China Civil Aviation Flight University of China Caac Laboratory of Flight Techniques and Flight Safety Guanghan China

ISBN: (纸本)9798350375909

This paper introduces a decision fusion strategy for automatic target recognition in multi-view synthetic aperture radar (SAR) images. Our proposed architecture integrates decisions derived from adaptive dictionary learning and Convolutional Neural Network (CNN) methods. Specifically, we employ two adaptive dictionary learning methods with distinct dictionary constructions to extract features from SAR images at each viewpoint, leading to intermediate decisions. Simultaneously, the CNN branch utilizes a multi-input single-output fully CNN to extract features from multiple viewpoints and make an intermediate decision. Subsequently, considering the multi-view images and leveraging the strengths of different methods, the classification results produced by these models are aggregated through voting to yield the final classification labels. Experimental results on the MSTAR dataset demonstrate the effectiveness of our proposed method, achieving an outstanding accuracy of 99.958% in a ten-class classification task. © 2024 IEEE.

关键词： Convolutional neural networks

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Estimation of the Number of Signals in White Noise via Large-Sample and High-Dimensional Asymptotic Theory 22

Estimation of the Number of Signals in White Noise via Large...

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22nd IEEE International Conference on Communication Technology, ICCT 2022

作者： Rong, Yao Zhang, Lewen Tang, Mengjiao Zan, Zeqiong Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan Kunming650504 China

ISBN: (纸本)9781665470674

This paper focuses on estimating the number of source signals embedded in Gaussian white noise. We address this problem via a sequence of nested hypothesis tests, and construct variance statistics based on eigenvalues of the sample covariance for each candidate hypothesis. Then, a detailed statistical analysis of these statistics is carried out in both the large-sample and high-dimensional regimes. According to this analysis, we propose a new scheme for determining the number of source signals in both the sample-rich and sample-starved cases. Finally, numerical examples are presented to show its superiority compared to some existing estimation methods. © 2022 IEEE.

关键词： Eigenvalues and eigenfunctions

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Differentially private data release for mixed-type data via latent factor models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 5682-5718页

作者： Yanqing Zhang Qi Xu Niansheng Tang Annie Qu Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Southwest United Graduate School Kunming China Department of Statistics University of California Irvine Irvine CA Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China

Differential privacy is a particular data privacy-preserving technology which enables synthetic data or statistical analysis results to be released with a minimum disclosure of private information from individual records. The tradeoff between privacy-preserving and utility guarantee is always a challenge for differential privacy technology, especially for synthetic data generation. In this paper, we propose a differentially private data synthesis algorithm for mixed-type data with correlation based on latent factor models. The proposed method can add a relatively small amount of noise to synthetic data under a given level of privacy protection while capturing correlation information. Moreover, the proposed algorithm can generate synthetic data preserving the same data type as mixed-type original data, which greatly improves the utility of synthetic data. The key idea of our method is to perturb the factor matrix and factor loading matrix to construct a synthetic data generation model, and to utilize link functions with privacy protection to ensure consistency of synthetic data type with original data. The proposed method can generate privacy-preserving synthetic data at low computation cost even when the original data is high-dimensional. In theory, we establish differentially private properties of the proposed method. Our numerical studies also demonstrate superb performance of the proposed method on the utility guarantee of the statistical analysis based on privacy-preserved synthetic data.

关键词： correlation factor model link function synthetic data

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Testing for Error Correlation in Semi-Functional Linear Models

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Journal of Systems Science & Complexity 2023年第4期36卷 1697-1716页

作者： YANG Bin CHEN Min ZHOU Jianjun Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan UniversityKunming 650091China City College Kunming University of Science and TechnologyKunming 650051China School of Mathematical Sciences Shanxi UniversityTaiyuan 030006China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China

Existing methods for analyzing semi-functional linear models usually assumed that random errors are not serially correlated or serially correlated with the known ***,in some applications,these assumptions on random errors may be unreasonable or *** this end,this paper aims at testing error correlation in a semi-functional linear model(SFLM).Based on the empirical likelihood approach,the authors construct an empirical likelihood ratio statistic to test the serial correlation of random errors and identify the order of autocorrelation if the serial correlation *** proposed test statistic does not need to estimate the variance as it is data adaptive and possesses the nonparametric version of Wilks'*** studies are conducted to investigate the performance of the proposed test *** real examples are illustrated by the proposed test method.

关键词： Empirical likelihood error correlation functional principal component analysis semifunctional linear model spline estimation Wilks'theorem

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Robust Low-Rank Matrix Completion Under Generalized Gaussian Mixture Model With Application to DoA Estimation

Robust Low-Rank Matrix Completion Under Generalized Gaussian...

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IET International Radar Conference 2023, IRC 2023

作者： Tang, Mengjiao Zan, Zeqiong Rong, Yao Hu, Sanfeng Li, Fan Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China CAAC Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan China

ISBN: (纸本)9781839539954

This paper considers the problem of noisy low-rank matrix completion (LRMC), presenting a novel and robust LRMC approach that differs from classical methods reliant solely on Lp-norms for handling single noise distributions. Instead, we adopt a model that accounts for the noise using a Mixture of Generalized Gaussian (MoGG) distributions'in both the real and complex circularly symmetric cases'thus accommodating more intricate noise patterns. Our proposed method formulates the LRMC problem as the minimization of the negative log-likelihood coupled with a nuclear norm penalty, which is effectively addressed by the EM-ADMM algorithm devised in this paper. Moreover, the proposed complex-valued LRMC method is applied to tackle the Direction of Arrival (DoA) estimation with incomplete measurements. Specifically, we leverage the developed LRMC technique for imputation of the missing entries and then employ the MUSIC algorithm to estimate the angles of impinging signals. Finally, numerical examples are presented to show the performance improvement of our proposed method compared to some classical ones. © The Institution of Engineering & Technology 2023.

关键词： COMPLEX CIRCULARLY SYMMETRIC DISTRIBUTIONS DOA ESTIMATION LOW-RANK MATRIX COMPLETION MIXTURE OF GENERALIZED GAUSSIAN DISTRIBUTIONS

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Joint Design of the Transmit and Receive Beamforming via Non-iterative Optimization for MIMO Radar

Joint Design of the Transmit and Receive Beamforming via Non...

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IET International Radar Conference 2023, IRC 2023

作者： Li, Yujia Du, Xiaolin Tang, Mengjiao Cui, Guolong Rong, Yao Yantai University Yantai China Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781839539954

The joint design of the transmit and receive beamforming for multiple-input multiple-output (MIMO) radar systems is considered in the background of signal dependent interference. An optimization model using signal-to-interference-plus-noise-ratio (SINR) as a performance metric is designed under similarity cone, power, and energy constraints. An algorithm for simultaneous updating of transmit and receive (T-R) beamforming is presented, that bypasses the conventional T-R iteration and initialization, thus avoiding premature termination of the iteration or poor initialization. Simulation experiments verify the effectiveness of the method. © The Institution of Engineering & Technology 2023.

关键词： MIMO radar

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Split-And-Merge Based Simultaneous Input and State Filter for Nonlinear Dynamic Systems

Split-And-Merge Based Simultaneous Input and State Filter fo...

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2022 Chinese Automation Congress, CAC 2022

作者： Hu, Sanfeng Guo, Liping Tang, Mengjiao Rong, Yao Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis China Sichuan University College of Mathematics China

ISBN: (纸本)9781665465335

This paper investigates the problem of simultaneous input and state estimation (SISE) for nonlinear dynamic systems. By the augmented state approach, we convert this problem into a standard filtering problem. Then, the split-And-merge technique is utilized for the augmented state estimation. Based on this, a novel split-And-merge based simultaneous input and state filter is developed in order to enhance the ability of dealing with highly nonlinear systems. Simulations demonstrate the effectiveness and efficiency of the proposed filter. © 2022 IEEE.

关键词： State estimation

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Rao Distance Based Spatial-Spectral Joint Feature Extraction and Semi-Supervised Classification for Hyperspectral Image 5

Rao Distance Based Spatial-Spectral Joint Feature Extraction...

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5th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2023

作者： Ni, Meixin Tang, Mengjiao Hu, Sanfeng Wang, Liyuqi Li, Fan Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan Kunming650504 China Civil Aviation Flight University of China CAAC Key Laboratory of Flight Techniques and Flight Safety Sichuan Guanghan618307 China

ISBN: (纸本)9798350329551

This paper presents a novel feature extraction approach for hyperspectral images, termed Spatial-Spectral Joint Principal Component analysis (PCA) coupled with Rao distance-based t-distributed Stochastic Neighbor Embedding (SSPCA-Rao-tSNE). Specifically, we apply the classical PCA method for spectral feature extraction while concurrently developing a Rao-distance-based tSNE algorithm to enhance the extraction of spatial features, particularly those with non-Euclidean structural characteristics. These spatial and spectral features are then combined through feature stacking to create spatial-spectral joint features. Furthermore, these spatial-spectral joint features are utilized in a semi-supervised graph convolutional network for hyperspectral image classification. Experimental results demonstrate the superior performance achieved by our newly proposed SSPCA-Rao-tSNE approach and the corresponding semi-supervised classification method in comparison to several classical methods. © 2023 IEEE.

关键词： Feature extraction

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Synchronization of delayed neural networks via intermittent sampled-data control

Synchronization of delayed neural networks via intermittent ...

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第43届中国控制会议

作者： Ying Yang Jifei Miao Xinyu Mei School of Mathematics and Statistics Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The paper investigates the synchronization problem for time-delay neural networks via intermittent sampled-data control. To fully consider the characteristics of the controlled systems, a mixed two-side-looped functional is constructed, and two inequalities are proposed to estimate the norm of the system state. After then, the synchronization criterion and the controller condition are obtained. To illustrate the effectiveness of the presented results, a numerical example is given.

关键词： Synchronization Neural networks Intermittent sampled-data control Time delay

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Evidence for Magneto-gravitational Processes in Supermassive Black Hole Binary PG 1553+113

arXiv

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arXiv 2025年

作者： Haiyun, Zhang Dahai, Yan Li, Zhang Niansheng, Tang Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming650091 China Department of Astronomy Key Laboratory of Astroparticle Physics of Yunnan Province Yunnan University Kunming650091 China

PG 1553+113 has drawn significant attention for its quasi-periodic oscillation (QPO) in Υ-ray variability, though the origin of its variability remains uncertain. In this study, we propose a physical mechanism to explain the observed Υ-ray variability within the framework of a supermassive black hole binary (SMBHB) system, supported by a newly identified component hidden in the light curve. A detailed analysis for its ∼16-year light curve obtained from Fermi-LAT observations is performed by Gaussian process (GP). As anticipated, the QPO of 2.1 years (771 ± 8 days) is effectively captured by the stochastically-driven damped simple harmonic oscillator (SHO) kernel within the under-damped regime, and the overall stochastic nature of the variability is described by the damped random walk (DRW) kernel albeit with an unconstrained damping timescale. Additionally, our results reveal a previously unrecognized component in active galactic nuclei variability, characterized by the Matérn−3/2 kernel, which is typically associated with systems undergoing abrupt energy release. These findings can be consistently interpreted within the SMBHB framework. The QPO of ∼2.1 years is likely attributed to the orbital motion in a SMBHB system. The Matérn−3/2 component is interpreted as resulting from magnetic reconnection events triggered by gravitational perturbations of the magnetic field within the jet, occurring as one black hole approaches the other. Meanwhile, in this case, the damping timescale of the common DRW kernel remains unconstrained due to the influence of new perturbations within the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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