Purpose This paper aims to investigate an identification strategy for the nonlinear state-space model (SSM) in the presence of an unknown output time-delay. The equations to estimate the unknown model parameters and o...
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Purpose This paper aims to investigate an identification strategy for the nonlinear state-space model (SSM) in the presence of an unknown output time-delay. The equations to estimate the unknown model parameters and output time-delay are derived simultaneously in the proposed strategy. Design/methodology/approach The unknown integer-valued time-delay is processed as a latent variable which is uniformly distributed in a priori known range. The estimations of the unknown time-delay and model parameters are both realized using the expectation-maximization (EM) algorithm, which has a good performance in dealing with latent variable issues. Moreover, the particle filter (PF) with an unknown time-delay is introduced to calculated the Q-function of the EM algorithm. Findings Although amounts of effective approaches for nonlinear SSM identification have been developed in the literature, the problem of time-delay is not considered in most of them. The time-delay is commonly existed in industrial scenario and it could cause extra difficulties for industrial process modeling. The problem of unknown output time-delay is considered in this paper, and the validity of the proposed approach is demonstrated through the numerical example and a two-link manipulator system. Originality/value The novel approach to identify the nonlinear SSM in the presence of an unknown output time-delay with EM algorithm is put forward in this work.
We present a generative factor analyzed hidden Markov model (GFA-HMM) for automatic speech recognition. In a standard HMM, observation vectors are represented by mixture of Gaussians (MoG) that are dependent on discre...
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We present a generative factor analyzed hidden Markov model (GFA-HMM) for automatic speech recognition. In a standard HMM, observation vectors are represented by mixture of Gaussians (MoG) that are dependent on discrete-valued hidden state sequence. The GFA-HMM introduces a hierarchy of continuous-valued latent representation of observation vectors, where latent vectors in one level are acoustic-unit dependent and latent vectors in a higher level are acoustic-unit independent. An expectationmaximization (EM) algorithm is derived for maximum likelihood estimation of the model. We show through a set of experiments to verify the potential of the GFA-HMM as an alternative acoustic modeling technique. In one experiment, by varying the latent dimension and the number of mixture components in the latent spaces, the GFA-HMM attained more compact representation than the standard HMM. In other experiments with varies noise types and speaking styles, the GFA-HMM was able to have (statistically significant) improvement with respect to the standard HMM, (c) 2005 Elsevier B.V. All rights reserved.
Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag informati...
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Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag information at the reader, however, is a challenging task due to the difficulty in acquiring the relevant channel-state information (CSI). To eliminate the need to estimate the CSI, in this paper, we propose a label-assisted transmission framework, in which two known labels are transmitted from the tag before data transmission. By exploring the received signal constellation information, we propose modulation-constrained expectation maximization algorithm, based on which two detection methods are developed. One method, referred to as constellation learning with labeled signals, learns the parameters by clustering the labeled signals and recovers the unlabeled signals by the learnt parameters. The other method, referred to as constellation learning with labeled and unlabeled signals, uses all received signals in clustering. Efficient initialization techniques are provided for the two clustering algorithms. Finally, extensive simulation results show that the proposed constellation learning methods achieve comparable performance as the optimal detector with perfect CSI.
This paper focuses on the robust parameters estimation algorithm of linear parameters varying (LPV) models. The classical robust identification techniques deal with the polluted training data, for example, outliers in...
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This paper focuses on the robust parameters estimation algorithm of linear parameters varying (LPV) models. The classical robust identification techniques deal with the polluted training data, for example, outliers in white noise. The paper extends this robustness to both symmetric and asymmetric noise with outliers to achieve stronger robustness. Without the assumption of Gaussian white noise pollution, the paper employs asymmetric Laplace distribution to model broader noise, especially the asymmetrically distributed noise, since it is an asymmetric heavy-tailed distribution. Furthermore, the asymmetric Laplace (AL) distribution is represented as the product of Gaussian distribution and exponential distribution to decompose this complex AL distribution. Then, a shifted parameter is introduced as the regression term to connect the probabilistic models of the noise and the predict output that obeys shifted AL distribution. In this way, the posterior probability distribution of the unobserved variables could be deduced and the robust parameters estimation problem is solved in the general expectation maximization algorithm framework. To demonstrate the advantage of the proposed algorithm, a numerical simulation example is employed to identify the parameters of LPV models and to illustrate the convergence.
A maximum a posteriori algorithm, which incorporates correlated magnetic resonance images into the processing of positron emission tomography reconstruction with the aim of improving image quality was developed. The l...
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A maximum a posteriori algorithm, which incorporates correlated magnetic resonance images into the processing of positron emission tomography reconstruction with the aim of improving image quality was developed. The line site map from MRI a priori is made up of a modified Markov random field or Canny edge detector with Gaussian smoothing filter. It is used in the MAP algorithm by a weighted line site method. We evaluate and compare the performance of these reconstruction methods. The results show that the Bayesian methods produce reconstructed images with less noise and better spatial resolution than those produced by the maximum likelihood-expectationmaximization method. (C) 2004 Elsevier Ltd. All rights reserved.
Term suggestion is a kind of information retrieval technique that attempts to suggest relevant terms to help users formulate more effective queries and reduce unnecessary search steps. In this paper, we apply two sema...
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Term suggestion is a kind of information retrieval technique that attempts to suggest relevant terms to help users formulate more effective queries and reduce unnecessary search steps. In this paper, we apply two semantic analysis methods, the probabilistic analysis model and semantic analysis graph, to design a term suggestion system that can effectively deal with the problems of synonymy and polysemy. The main contributions of this paper are the following. First, we apply two semantic analysis methods to design a high-performance term suggestion system. Second, we design an intelligent mechanism that can effectively balance cost and performance to minimize the number of iterations required for our system. (C) 2012 Elsevier B.V. All rights reserved.
作者：
Kubota, TakuyaAritsugi, MasayoshiKumamoto Univ
Grad Sch Sci & Technol Comp Sci & Elect Engn Chuo Ku 2-39-1 Kurokami Kumamoto 8608555 Japan Kumamoto Univ
Fac Adv Sci & Technol Div Environm Sci Big Data Sci & TechnolChuo Ku 2-39-1 Kurokami Kumamoto 8608555 Japan
It is expected that ground truths can result in many good labels in the crowdsourcing of labeling tasks. However, the use of ground truths has so far not been adequately addressed. In this paper, we develop algorithms...
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It is expected that ground truths can result in many good labels in the crowdsourcing of labeling tasks. However, the use of ground truths has so far not been adequately addressed. In this paper, we develop algorithms that determine the number of ground truths that are necessary. We determine this number by iteratively calculating the expected quality of labels for tasks with various sets of ground truths, and then comparing the quality with the limit of the estimated label quality expected to be obtained by crowd sourcing. We assume that each worker has a different unknown labeling ability and performs a different number of tasks. Under this assumption, we develop assignment strategies for ground truths based on the estimated confidence intervals of the workers. Our algorithms can utilize different approaches based on the expectationmaximization to estimate good-quality consensus labels. An experimental evaluation demonstrates that our algorithms work well in various situations. (C) 2016 Elsevier Inc. All rights reserved.
The performance of several regression methods is investigated to estimate the distribution of engineering demand parameters conditioned on intensity measures (EDP|IM) for small record sets. In particular, the performa...
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The performance of several regression methods is investigated to estimate the distribution of engineering demand parameters conditioned on intensity measures (EDP|IM) for small record sets. In particular, the performance of the multivariate ordinary least squares (OLS), a simultaneous mean-variance regression (MVR) done by a penalized weighted least-square loss function, and a mean-covariance/variance regression based on expectationmaximization method (EM) are assessed. The efficiency of the introduced methods is compared with FEMA-P58 methodology. Performance assessment of EM and MVR methods shows that the overall increase in efficiency is about 25-45% for maximum inter-story drift ratios, and 30-50% for maximum absolute floor acceleration.
A new Gaussian mixture model (GMM) has been developed for better representations of both atomic models and electron microscopy 3D density maps. The standard GMM algorithm employs an EM algorithm to determine the param...
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A new Gaussian mixture model (GMM) has been developed for better representations of both atomic models and electron microscopy 3D density maps. The standard GMM algorithm employs an EM algorithm to determine the parameters. It accepted a set of 3D points with weights, corresponding to voxel or atomic centers. Although the standard algorithm worked reasonably well;however, it had three problems. First, it ignored the size (voxel width or atomic radius) of the input, and thus it could lead to a GMM with a smaller spread than the input. Second, the algorithm had a singularity problem, as it sometimes stopped the iterative procedure due to a Gaussian function with almost zero variance. Third, a map with a large number of voxels required a long computation time for conversion to a GMM. To solve these problems, we have introduced a Gaussian-input GMM algorithm, which considers the input atoms or voxels as a set of Gaussian functions. The standard EM algorithm of GMM was extended to optimize the new GMM. The new GMM has identical radius of gyration to the input, and does not suddenly stop due to the singularity problem. For fast computation, we have introduced a downsampled Gaussian functions (DSG) by merging neighboring voxels into an anisotropic Gaussian function. It provides a GMM with thousands of Gaussian functions in a short computation time. We also have introduced a DSG-input GMM: the Gaussian-input GMM with the DSG as the input. This new algorithm is much faster than the standard algorithm.
In this paper, we investigate the performance of the maximum likelihood (ML) method to estimate the parameters of compound Nakagami-gamma shadowed fading channels. We derived analytically the ML estimates of the Nakag...
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In this paper, we investigate the performance of the maximum likelihood (ML) method to estimate the parameters of compound Nakagami-gamma shadowed fading channels. We derived analytically the ML estimates of the Nakagami-gamma distribution by using the expectation-maximization (EM) algorithm. Numerical simulations demonstrate the outperformance of the EM algorithm in terms of accuracy versus the moment-based estimation technique. Moreover, the EM iterations converge to the global optimum.
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