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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Lewen Rong, Yao Tang, Mengjiao Du, Xiaolin Li, Fan Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China School of Computer and Control Engineering Yantai University Yantai China CAAC Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan China

ISBN: (纸本)9781839539954

This paper provides a novel estimation procedure for determining the number of high-dimensional complex-valued signals embedded in Gaussian white noise. Initially, this problem is formulated as a sequence of nested hypothesis tests. To enhance the decision performance, we introduce new test statistics based on the ratio of the fourth- and square second-order moments of the population covariance eigenvalues. This enables us to leverage higher-order moments information in the observed data, distinguishing it from conventional tests that rely solely on the first and second order moments. In the context of high-dimensional, complex-valued data and within a sub-sphericity testing framework, unbiased and consistent estimates are derived for these two higher-order moments. Subsequently, we conduct an asymptotic analysis to incoporate these estimates into the construction of the test statistics and the design of our proposed estimation scheme for determining the number of sources. Finally, numerical examples are presented to show its effectiveness and superiority over several classical estimation methods. © The Institution of Engineering & Technology 2023.

关键词： White noise

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data Classification Algorithms for Complex Heavy-Tailed Distributions

Data Classification Algorithms for Complex Heavy-Tailed Dist...

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2021 CIE International Conference on Radar, Radar 2021

作者： Li, Jianbo Rong, Yao Yang, Liu Liang, Zhifang Du, Xiaolin School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Yunnan University Yunnan Key Laboratory of Statistical Modeling and Data Analysis Kunming650504 China School of Computer and Control Engineering Yantai University Yantai264005 China

ISBN: (纸本)9781665498142

This paper deals with the adaptive classification problem of heavy-tailed data drawn from a multivariate distribution of the complex elliptically symmetric family. The authors assume that the collected data is either homogeneous or partially homogeneous. Based on the GLRT criterion, two adaptive classifiers are devised for the scenarios of Hermitian and persymmetric scatter matrices, respectively. To estimate the unknown parameters, iterative algorithms with convergence property are introduced to solve the developed non-convex maximum likelihood optimization problems. The simulation results show that, compared with the Hermitian scenario, the exploitation of persymmetric structure information can lead to an obvious performance improvement. © 2021 IEEE.

关键词： Classification GLRT heavy-tailed distribution majorization-minimization persymmetric structure

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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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Chinese Automation Congress (CAC)

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

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.

关键词： Automation Filtering Computational modeling Nonlinear dynamical systems Computational efficiency Kalman filters State estimation

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Algorithms of Time Series Network: Approaches Reproduction and Networks Topology 9

Algorithms of Time Series Network: Approaches Reproduction a...

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9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Wang, Li-Na Cheng, Yuan-Yuan Tan, Gui-Min Science College Inner Mongolia University of Technology Hohhot010051 China Inner Mongolian Key Lab. of Statistical Analysis Theory for Life Data and Neural Network Modeling Hohhot010051 China

ISBN: (纸本)9781643684703

Network analysis methods of time series provide a new analytical framework on describing complex behaviors using sample data. Reproducibility is one of the important principles in scientific research. This work focuses on the algorithm review and reproduction of time series network. We paid special attention to the applicability and data characteristics of the five typical algorithms: visibility graph, phase space reconstruction, fluctuation mode, symbolic representation and coarsened multidimensional time series. It provides a reference and inspiration for future analysis of time series with various characteristics. Although two pioneer methods are widely applicable, while the directed weighted network established by coarsening thoughts contains more information about time series. In addition, coarseness process makes these approaches perform better for massive data analysis. It should be noted that: the process of data coarsening needs to ensure that the data characteristics of the original time series are inherited. © 2023 The authors and IOS Press.

关键词： Time series

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Improved Far-field Speaker Recognition Method Based Geometry Acoustic Simulation and SpecAugment

Improved Far-field Speaker Recognition Method Based Geometry...

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2021 International Conference on Intelligent Computing, Automation and Applications, ICAA 2021

作者： Lian, Yingzi Pang, Jing Inner Mongolia University of Technology College of Science Hohhot China Inner Mongolia Key Lab. of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9781665437301

Deep learning method has made a great breakthrough in the field of speaker recognition. However, the performance of deep learning method based neural network often degrade in far-field speaker recognition task, with respect to the near-field. One of the indispensable factors is that deep learning performance has a great impact on the quality and diversity of data. Utterance in far-field scenario always mixed with variant reverberation and noise which could interfere the prediction of model. In this paper, we proposed a novel method comprised of geometry acoustic simulation (GAS) and SpecAugment, which makes the performance of deep learning method on far-field speaker recognition task more stable and excellent. Based on near-field speech dataset, by means of physical simulation, far-field speech with different types and characters can be generated to provide as many scenes as possible for speaker recognition. Simultaneously, the SpecAugment method has been applied to enhance the diversity of utterance data on mel-spectrogram. Then, ResNet34 and AM-Softmax Loss were used as the most commonly used methods for feature extraction and model training. Our proposed method has been trained on Voxceleb1 and V oxceleb2 dataset, and test on Vox celeb original test set. Evaluations show that our proposed method has 46% and 40% of EER and minDCF relatively improvement on far-field speaker recognition task. © 2021 IEEE.

关键词： Deep learning

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Combining Experimental and Historical data for Policy Evaluation 41

Combining Experimental and Historical Data for Policy Evalua...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Ting Shi, Chengchun Wen, Qianglin Sui, Yang Qin, Yongli Lai, Chunbo Zhu, Hongtu School of Statistics and Management Shanghai University of Finance and Economics China Department of Statistics London School of Economics and Political Science United Kingdom Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University China Didi Chuxing China Department of Biostatistics The University of North Carolina Chapel Hill United States

This paper studies policy evaluation with multiple data sources, especially in scenarios that involve one experimental dataset with two arms, complemented by a historical dataset generated under a single control arm. We propose novel data integration methods that linearly integrate base policy value estimators constructed based on the experimental and historical data, with weights optimized to minimize the mean square error (MSE) of the resulting combined estimator. We further apply the pessimistic principle to obtain more robust estimators, and extend these developments to sequential decision making. Theoretically, we establish non-asymptotic error bounds for the MSEs of our proposed estimators, and derive their oracle, efficiency and robustness properties across a broad spectrum of reward shift scenarios. Numerical experiments and real-data-based analyses from a ridesharing company demonstrate the superior performance of the proposed estimators. Copyright 2024 by the author(s)

关键词： data assimilation

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Application of statistical shape models in orthopedics: a narrative review

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Intelligent Medicine 2024年第4期4卷 249-255页

作者： Xingbo Cai Ying Wu Junshen Huang Long Wang Yongqing Xu Sheng Lu Postgraduate College of Kunming Medical University KunmingYunnan 650500China Department of Orthopedics The First People’s Hospital of Yunnan ProvinceKunmingYunnan 650032China Key Lab of Statistical Modeling and Data Analysis of Yunnan Yunnan UniversityKunmingYunnan 650091China Yunnan Provincial Key Laboratory of Digital Orthopedics KunmingYunnan 662299China Lvweijia Expert Workstation KunmingYunnanChina Department of Orthopedics 920 Hospital of Chinese People’s Liberation Army Joint Logistic Support ForceKunmingYunnan 650032China

statistical shape models(SSMs)are effective for image processing and analysis and have been used in various medical fields,including face recognition and cranial bone *** orthopedics,SSMs are being used in numerous applications,such as automated segmentation of medical images,preoperative planning,intraoper-ative navigation combined with robotics,simulation reconstruction of defects,human biomechanics research,description of anatomical shape changes,and prosthesis *** review highlighted the wide range of ap-plications while acknowledging the diversity of methodologies and techniques encompassed by SSMs,including Gaussian process models and nonlinear *** addition,the available software packages for constructing shape models,such as Scalismo,ShapeWorks,and Deformetrica,were discussed.

关键词： statistical shape analysis Orthopedics Automated diagnosis support data reconstruction Segmentation

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Adaptive Radar Detection in Compound-Gaussian Environments with Missing data

Adaptive Radar Detection in Compound-Gaussian Environments w...

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Signal, Information and data Processing (ICSIDP), IEEE International Conference on

作者： Chenchen Hao Yao Rong Mengjiao Tang Fan Li Xin Wang Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

This paper addresses adaptive radar detection in scenarios with incomplete observations due to measurement errors, sensor failures, or outliers, where the target is embedded in compound Gaussian clutter with unknown covariance matrix. The detection problem is formulated as a binary hypothesis test, with a set of selection matrices introduced to represent the observations at each snapshot. At the stage of detector design, a two-step design strategy is employed: first, the Generalized Likelihood Ratio Test (GLRT) is derived assuming known covariance structure; then, the actual covariance matrix in the GLRT is replaced with its maximum likelihood estimate, which is calculated based on the non-missing components of the secondary data using an expectation-maximization (EM) algorithm developed in this paper. Numerical examples demonstrate the effectiveness of these detectors in compound Gaussian clutter environments, showing superior performance compared to the existing detector designed for scenarios with Gaussian interference and missing data, as well as the classical linear imputation method.

关键词： Performance evaluation Maximum likelihood estimation Measurement errors Radar detection Detectors Radar Radar signal processing Compounds Covariance matrices Clutter

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Adaptive Subspace Detection of Range-Distributed Targets in Compound Gaussian Clutter with Generalized Inverse Gaussian Texture

Adaptive Subspace Detection of Range-Distributed Targets in ...

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Signal, Information and data Processing (ICSIDP), IEEE International Conference on

作者： Xin Wang Yao Rong Mengjiao Tang Fan Li Chenchen Hao Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

This paper addresses adaptive subspace detection of range-distributed targets in compound Gaussian clutter with Generalized Inverse Gaussian (GIG) texture. Novel adaptive subspace detectors are developed based on the generalized likelihood ratio, Rao, and Wald test criteria, employing two-step design strategies that incorporate the persymmetric covariance structure and the GIG texture distribution to enhance detection performance. Furthermore, to achieve full adaptivity, a novel method is proposed for adaptively estimating the clutter texture parameters. The effectiveness of the proposed adaptive detectors, combined with this new texture parameter estimation method, is validated via numerical examples in various non-Gaussian clutter environments, demonstrating superior detection performance compared to several existing methods.

关键词： Parameter estimation Estimation Detectors data processing Compounds Clutter Covariance matrices

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

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

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Geoscience and Remote Sensing Mapping (GRSM), International Conference on

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

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.

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