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6th International Conference on Artificial Intelligence and Computer Applications, ICAICA 2024

作者： Zu, Le Kang, Yong Gao, Jie He, Shanshan School of General Education Wannan Medical College Wuhu241002 China

ISBN: (纸本)9798350377255

Language recognition technology plays a crucial role in automated speech processing within multilingual environments, particularly under the globalized context where the increasing linguistic diversity poses higher demands on language recognition systems. Due to their robustness and efficiency, segment-level features and automatic identification algorithms have gradually become focal points of research. This paper explores a variety of language signal feature parameters, including RASTA-PLP parameters, MFCC parameters, RASTA-MFCC parameters, and CMS-MFCC parameters, as well as extraction methods for sliding difference cepstrum parameters and their applications in language recognition. Gaussian Mixture Models, along with their improved expectation-maximization algorithms and greedy expectation-maximization algorithms, are thoroughly analyzed to enhance the accuracy and robustness of language recognition. Preliminary experiments verify the performance of different feature parameters and algorithms on actual datasets, demonstrating that RASTA and CMS techniques significantly enhance the MFCC parameters. © 2024 IEEE.

关键词： expectation maximization algorithm

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Structure-coupled Variational Bayesian Method for Building Layout Reconstruction

Structure-coupled Variational Bayesian Method for Building L...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Yin, Zixiang Zeng, Xiaolu Yang, Xiaopeng Liao, Jiancheng Gong, Junbo Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314001 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798350375909

This paper presents a structure-coupled sparse Bayesian learning method for building layout reconstruction using through-the-wall radar. We characterize the azimuth continuity of the wall and two-dimensional extensibility of corner into a hierarchical probabilistic model. Moreover, we employ a variational expectation maximization algorithm to perform Bayesian inference, which provides an internal building structure map. Meanwhile, a generalized approximate message passing algorithm is integrated to speed up operations by avoiding matrix inversion. Results based on real data validate that the proposed method can extract the walls and corners accurately, thus enabling building layout reconstruction completely. © 2024 IEEE.

关键词： expectation maximization algorithm

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Remaining Useful Life Prediction of Railway Balise Based on Wiener Process Under Varying Temperature Conditions 27

Remaining Useful Life Prediction of Railway Balise Based on ...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Li, Zhengjiao Zhao, Zishuo Liu, Jiang Cai, Baigen Lu, Debiao School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798331505929

The balise transmission system (BTS) plays a vital role in ensuring reliable ground communication of railway traffic. However, due to the natural loss of circuit operation, harsh working environment and high operating frequency, its circuit components gradually degrade and lead to system failure. Combined with the significant influence of varying temperature on the degradation of circuit system, this paper proposes a new method for predicting the remaining useful life (RUL) of railway balise. Firstly, a Wiener degradation model based on the Arrhenius temperature model is proposed, which can calculate the degradation path curve at different temperatures. Secondly, a model parameter estimation method combining expectation maximization (EM) algorithm and Kalman filter algorithm is proposed. Then, the probability density function (PDF) of the balise RUL under varying temperature conditions is derived. Finally, the effectiveness of the proposed method is verified by an example. The results show that the proposed prediction method has higher accuracy. © 2024 IEEE.

关键词： expectation maximization algorithm

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Adaptive Radar Detection in Compound-Gaussian Environments with Missing Data 2

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

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

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

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： expectation maximization algorithm

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Two-Dimensional Off-Grid DOA Estimation Based on Sparse Bayesian Learning 12

Two-Dimensional Off-Grid DOA Estimation Based on Sparse Baye...

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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Qin, Jiale Gui, Shuliang Tian, Zengshan Xiao, Ke Tian, Haitao Chongqing University of Posts and Telecommunications Chongqing400065 China Guangdong Institute of Electronic Information Engineering University of Electronic Science and Technology of China dongguan523000 China

ISBN: (纸本)9798350351019

In this paper, a two-dimensional off-grid direction of arrival (DOA) estimation strategy based on sparse Bayesian learning (SBL) is proposed for improving the two-dimensional DOA estimation accuracy of millimeter-wave multiple-input multiple-output (MIMO) radar in low signal-to-noise ratio (SNR) environment. First, the grid is employed alongside Taylor expansion to formulate the signal model. After that, the expectation maximization (EM) algorithm is employed in conjunction with the corresponding marginal probability density function to iteratively estimate the hyperparameters. Finally, the conditional probability density function of the received signal is utilized, from which the error weighting coefficients for off-grid DOA estimation are derived. The simulation combined with the data of Cascade MIMO radar demonstrates the effectiveness of the proposed method relative to other algorithms. © 2024 IEEE.

关键词： expectation maximization algorithm

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Accurate Delayed Source Model for Multi-Frame Full-Rank Spatial Covariance Analysis 18

Accurate Delayed Source Model for Multi-Frame Full-Rank Spat...

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18th International Workshop on Acoustic Signal Enhancement, IWAENC 2024

作者： Furunaga, Shinya Sawada, Hiroshi Ikeshita, Rintaro Nakatani, Tomohiro Makino, Shoji Waseda University 2-7 Hibikino Wakamatsu-ku Kitakyushu Fukuoka808-0135 Japan Ntt Corporation 2-4 Hikaridai Seika-cho Soraku-gun Kyoto619-0237 Japan

ISBN: (纸本)9798350361858

Multi-frame Full-rank Spatial Covariance Analysis (mfFCA) is a technique for a blind source separation method and can be applied to reverberant underdetermined conditions where the sources outnumber the microphones and the reverberation time is long. This model, however, does not express all direct and delayed source components in multi-frame observation vector. This paper proposes a new model that takes into account accurately the direct and delayed source components, by introducing delay-wise spatial covariance matrices. We have then derived new expectation-maximization and multiplicative update algorithms for the proposed model. Experimental results show that the proposed method performed better than the conventional mfFCA for the task to separate three sources with two microphones. © 2024 IEEE.

关键词： expectation maximization algorithm

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Accelerating the convergence of EM-based training algorithms for RBF networks

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6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001

作者： Lázaro, Marcelino Santamaría, Ignacio Pantaleón, Carlos Dpto. Ing. Comunieaciones ETSII v Telecom Universidad de Cantabria Avda Los Castros 39005 Santander Spain

ISBN: (纸本)3540422358

In this paper, we propose a new expectation-maximization (EM) algorithm which speeds up the training of feedforward networks with local activation functions such as the Radial Basis Function (RBF) nctw ork. The core of the conditional EM algorithm for supervised learning of feedforward net w orks consists of decomposing the obserations into their individual output units and then estimating the parameters of each unit separately. In previously proposed approaches, at eac h *** the residual is decomposed equally among the units or proportionally to the weights of the output layer. However, this approach tends to slow down the training of networks with local activation units. To overcome this drawback in this paper we use a new E-step which applies a soft decomposition of the residual among the units. Inparticular, the residual is decomposed according to the probability of each RBF unit given each input-output pattern. It is shown that this variant not only speeds up the training in comparison with other EM-type1 algorithms, but also provides better results than a global gradient-descent, technique since it has the capability of avoiding some unw anted minima of the cost function. © Springer-Verlag Berlin Heidelberg 2001.

关键词： expectation maximization algorithm

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Unsupervised ensemble learning for genome sequencing

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PATTERN RECOGNITION 2022年 129卷

作者： Pages-Zamora, Alba Ochoa, Idoia Cavero, Gonzalo Ruiz Villalvilla-Ornat, Pol Univ Politecn Catalunya BarcelonaTech UPC SPCOM Grp C-Jordi Girona 31 Barcelona 08034 Spain Univ Navarra Tecnun Manuel Lardizabal 13 San Sebastian 20018 Spain Swiss Fed Inst Technol Erdbeobachtung u Fernerkundung HCP G 33-1Leopold Ruzicka Weg 4 CH-8093 Zurich Switzerland

Unsupervised ensemble learning refers to methods devised for a particular task that combine data pro-vided by decision learners taking into account their reliability, which is usually inferred from the data. Here, the variant calling step of the next generation sequencing technologies is formulated as an unsuper-vised ensemble classification problem. A variant calling algorithm based on the expectation-maximization algorithm is further proposed that estimates the maximum-a-posteriori decision among a number of classes larger than the number of different labels provided by the learners. Experimental results with real human DNA sequencing data show that the proposed algorithm is competitive compared to state-of -the-art variant callers as GATK, HTSLIB, and Platypus.(c) 2022 The Author(s). Published by Elsevier *** is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： expectation maximization algorithm Variant calling Genome sequencing Unsupervised multi-class ensemble classifier GATK

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A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data

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BMC BIOINFORMATICS 2004年第1期5卷 201-201页

作者： Najarian, K Zaheri, M Rad, AA Najarian, S Dargahi, J Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA Amirkabir Univ Technol Comp Engn & IT Dept Tehran Iran Concordia Univ CONCAVE Res Ctr Dept Mech & Ind Engn Quebec City PQ Canada

Background: The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. Mixture model method (MMM hereafter) is a nonparametric statistical method often used for microarray processing applications, but is known to over-fit the data if the number of replicates is small. In addition, the results of the MMM may not be repeatable when dealing with a small number of replicates. In this paper, we propose a new version of MMM to ensure the repeatability of the results in different runs, and reduce the sensitivity of the results on the parameters. Results: The proposed technique is applied to the two different data sets: Leukaemia data set and a data set that examines the effects of low phosphate diet on regular and Hyp mice. In each study, the proposed algorithm successfully selects genes closely related to the disease state that are verified by biological information. Conclusion: The results indicate 100% repeatability in all runs, and exhibit very little sensitivity on the choice of parameters. In addition, the evaluation of the applied method on the Leukaemia data set shows 12% improvement compared to the MMM in detecting the biologically-identified 50 expressed genes by Thomas et al. The results witness to the successful performance of the proposed algorithm in quantitative pathogenesis of diseases and comparative evaluation of treatment methods.

关键词： Bayesian Information Criterion expectation maximization Acute Myeloid Leukaemia Acute Lymphoblastic Leukaemia expectation maximization algorithm

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Adaptive quantile low-rank matrix factorization

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PATTERN RECOGNITION 2020年 103卷 107310-107310页

作者： Xu, Shuang Zhang, Chun-Xia Zhang, Jiangshe Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China

Low-rank matrix factorization (LRMF) has received much popularity owing to its successful applications in both computer vision and data mining. By assuming noise to come from a Gaussian, Laplace or mixture of Gaussian distributions, significant efforts have been made on optimizing the (weighted) L-1 or L-2-norm loss between an observed matrix and its bilinear factorization. However, the type of noise distribution is generally unknown in real applications and inappropriate assumptions will inevitably deteriorate the behavior of LRMF. On the other hand, real data are often corrupted by skew rather than symmetric noise. To tackle this problem, this paper presents a novel LRMF model called AQ-LRMF by modeling noise with a mixture of asymmetric Laplace distributions. An efficient algorithm based on the expectation-maximization (EM) algorithm is also offered to estimate the parameters involved in AQ-LRMF. The AQ-LRMF model possesses the advantage that it can approximate noise well no matter whether the real noise is symmetric or skew. The core idea of AQ-LRMF lies in solving a weighted L-1 problem with weights being learned from data. The experiments conducted on synthetic and real data sets show that AQ-LRMF outperforms several state-of-the-art techniques. Furthermore, AQ-LRMF also has the superiority over the other algorithms in terms of capturing local structural information contained in real images. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Low-rank matrix factorization Mixture of asymmetric Laplace distributions expectation maximization algorithm Skew noise

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