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Parametric Pulse Train De-Interleaving of Stochastic Sources

IFAC Proceedings Volumes 1996年第1期29卷 4231-4236页

作者： Andrew Logothetis Vikram Krishnamurthy H. Vincent Poor CRC for Sensor Signal and Information Processing Department of Electrical and Electronic Engineering University of Melbourne Parkville Victoria 3052 Australia Department of Electrical Engineering Princeton University Princeton NJ 08544-5263 USA

In this paper we consider de-interleaving a finite number of stochastic parametric sources. The sources are modeled as independent autoregressive (AR) processes. Based on a Markovian switching policy, we assume that the different sources transmit signals on the same single channel. The receiver records the 1-bit quantized version of the transmitted signal and aims to identify the sequence of active sources. Once the source sequence has been identified, the characteristics (parameters) of each source is estimated.

关键词： Autoregressive Models Parameter Estimation expectation maximization algorithm Hidden Markov Models Binary Time Series

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Multi-model approach to nonlinear system identification with unknown time delay

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IFAC Proceedings Volumes 2014年第3期47卷 9388-9393页

作者： Lei Chen Biao Huang Fei Liu Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi 214122 P.R. China Department of Chemical and Materials Engineering University of Alberta Edmonton AB T6G 2G6 Canada

This paper is concerned with identification of nonlinear systems with a noisy scheduling variable, and the measurement of the system has an unknown time delay. Auto regressive exogenous (ARX) models are selected as the local models, and multiple local models are identified along the process operating points. The dynamics of a nonlinear system are represented by associating a normalized exponential function with each of the ARX models; therein, the normalized exponential function is acted as the probability density function. The parameters of the ARX models and the exponential functions as well as the unknown time delay are estimated simultaneously under the expectation maximization (EM) algorithm using the retarded input-output data. A CSTR example is given to verify the proposed identification approach.

关键词： Nonlinear system identification expectation maximization algorithm Multiple models Time delay

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System Identification from Multi-Rate Data

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IFAC Proceedings Volumes 2004年第1期37卷 155-160页

作者： R. Bhushan Gopaluni Harigopal Raghavan Sirish L. Shah Department of Chemical & Materials Engineering University of Alberta Edmonton AB CANADA - T6G 2G6

In this paper, we provide a novel iterative identification algorithm for multi-rate sampled data systems. The procedure involves, as a first step, identifying a simple initial model from multi-rate data. Based on this model, the "missing" data points in the slow sampled measurements are estimated following the expectation maximization approach. Using the estimated missing data points and the original data set, a new model is obtained and this procedure is repeated until the models converge. An attractive feature of the proposed method lies in its applicability to irregularly sampled data. An application of the proposed method to an industrial data set is also included.

关键词： identification multi-rate processes expectation maximization algorithm

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Nonlinear budget set regressions in random utility models: Theory and application to taxable income

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Journal of Econometrics 2024年

作者： Blomquist, Soren Kumar, Anil Liang, Che-Yuan Newey, Whitney K. Uppsala Center for Fiscal studies Department of Economics Uppsala University Sweden Department of Economics University of Iowa United States MIT Department of Economics United States NBER United States

This paper is about the nonparametric regression of a choice variable on a nonlinear budget set under utility maximization with general heterogeneity, i.e. in the random utility model (RUM). We show that utility maximization and convex budget sets make this regression three dimensional with a more parsimonious specification than previously derived. We show that nonconvexities in the budget set will have little effect on these results in important cases. We characterize all the restrictions of utility maximization on the budget set regression and show how to check these restrictions in applications. We formulate budget set effects that can be identified by this regression and give automatic debiased machine learners of these effects. We consider use of control functions to allow for endogeneity. Throughout we take as the main example the effect of taxes on taxable income including accounting for productivity growth. In an application to Swedish data we find the taxable income elasticity of a change in the slope of each segment to be .52, that the regression satisfies the restrictions of utility maximization at the values chosen for over 95% of observations, and that a productivity growth rate we estimate is close to other estimates. © 2024 Elsevier B.V.

关键词： expectation maximization algorithm

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AGSEI: Adaptive Graph Structure Estimation with Long-Tail Distributed Implicit Graphs

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IEEE Transactions on Emerging Topics in Computing 2024年

作者： He, Yunfei Wu, Yang Huang, Lishan Peng, Zhenwan Yang, Fei Zhang, Yiwen Sheng, Victor S Anhui Medial University School of Biomedical Engineering China Anhui University School of Computer Science and Technology China Texas Tech University School of Department of Computer Science United States

Empowered by their remarkable advantages, graph neural networks (GNN) serve as potent tools for embedding graph-structured data and finding applications across various domains. Particularly, a prevalent assumption in most GNNs is the reliability of the underlying graph structure. This assumption, often implicit, can inadvertently lead to the propagation of misleading information through structures like false links. In response to this challenge, numerous methods for graph structure learning (GSL) have been developed. Among these methods, one popular approach is to construct a simple and intuitive K-nearest neighbor (KNN) graph as a sample to infer true graph structure. However, KNN graphs that follow the single-point distribution can easily mislead the true graph structure estimation. The primary reason is that, from a statistical perspective, the KNN graph, as a sample, follows a single-point distribution, whereas the true graph structure, as the population, as a whole mostly follows a long-tail distribution. In theory, the sample and the population should share the same distribution;otherwise, accurately inferring the true graph structure becomes challenging. To address this problem, this paper proposes an Adaptive Graph Structure Estimation with Long-Tail Distributed Implicit Graph, referred to as AGSEI. AGSEI comprises three main components: long-tail implicit graph construction, explicit graph structure estimation, and joint optimization. The first component relies on a multi-layer graph convolutional network to learn low-order to high-order node representations, compute node similarity, and construct several corresponding long-tail implicit graphs. Since the original imperfect graph structure can mislead GNNs into propagating false information, it reduces the reliability of the long-tail implicit graphs. AGSEI attempts to limit the aggregation of irrelevant information by introducing the Hilbert-Schmidt independence criterion. That is, maximizing the dependenc

关键词： expectation maximization algorithm

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Enabling multiplexed testing of pooled donor cells through whole-genome sequencing

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GENOME MEDICINE 2018年第1期10卷 31-31页

作者： Chan, Yingleong Chan, Ying Kai Goodman, Daniel B. Guo, Xiaoge Chavez, Alejandro Lim, Elaine T. Church, George M. Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA Harvard Med Sch Dept Genet Boston MA 02115 USA Harvard MIT Hlth Sci & Technol Cambridge MA 02139 USA Columbia Univ Coll Phys & Surg Dept Pathol & Cell Biol 630 W 168th St New York NY 10032 USA

We describe a method that enables the multiplex screening of a pool of many different donor cell lines. Our method accurately predicts each donor proportion from the pool without requiring the use of unique DNA barcodes as markers of donor identity. Instead, we take advantage of common single nucleotide polymorphisms, whole-genome sequencing, and an algorithm to calculate the proportions from the sequencing data. By testing using simulated and real data, we showed that our method robustly predicts the individual proportions from a mixed-pool of numerous donors, thus enabling the multiplexed testing of diverse donor cells en masse. More information is available at https://***/poolseq/

关键词： Multiplexed testing Barcode free method Single nucleotide polymorphisms expectation maximization algorithm Next-generation sequencing Personal Genome Project

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Refining motifs by improving information content scores using neighborhood profile search

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algorithmS FOR MOLECULAR BIOLOGY 2006年第1期1卷 23-23页

作者： Reddy, Chandan K. Weng, Yao-Chung Chiang, Hsiao-Dong Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences (e.g. transcription factor binding sites in a genome). The most widely used algorithms for finding motifs obtain a generative probabilistic representation of these over-represented signals and try to discover profiles that maximize the information content score. Although these profiles form a very powerful representation of the signals, the major difficulty arises from the fact that the best motif corresponds to the global maximum of a non-convex continuous function. Popular algorithms like expectation maximization (EM) and Gibbs sampling tend to be very sensitive to the initial guesses and are known to converge to the nearest local maximum very quickly. In order to improve the quality of the results, EM is used with multiple random starts or any other powerful stochastic global methods that might yield promising initial guesses ( like projection algorithms). Global methods do not necessarily give initial guesses in the convergence region of the best local maximum but rather suggest that a promising solution is in the neighborhood region. In this paper, we introduce a novel optimization framework that searches the neighborhood regions of the initial alignment in a systematic manner to explore the multiple local optimal solutions. This effective search is achieved by transforming the original optimization problem into its corresponding dynamical system and estimating the practical stability boundary of the local maximum. Our results show that the popularly used EM algorithm often converges to suboptimal solutions which can be significantly improved by the proposed neighborhood profile search. Based on experiments using both synthetic and real datasets, our method demonstrates significant improvements in the information content scores of the probabilistic models. The proposed method also gives the flexibility in using different local solvers and global

关键词： expectation maximization expectation maximization algorithm Exit Point Motif Finding Local Optimal Solution

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Semi-automatic segmentation of myocardium at risk from contrast enhanced SSFP images - validation against manual delineation and SPECT

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Journal of Cardiovascular Magnetic Resonance 2015年第SUPPL 1期17卷 Q127-Q127页

作者： Jane Tufvesson Marcus Carlsson Anthony H Aletras Henrik Engblom Jean-Francois Deux Sasha Koul Peder Sörensson John Pernow Dan Atar David Erlinge Håkan Arheden Einar Heiberg Cardiac MR group Lund Dept. of Clinical Physiology Lund University Lund Sweden Dept of Numerical Analysis Faculty of Engineering Lund University Lund Sweden Dept of Cardiology Henri Mondor Hospital Creteil France Dept of Cardiology Lund University Lund Sweden Dept of Medicine Karolinska Institutet Karolinska University Hospital Stockholm Sweden Dept of Cardiology B Oslo University Hospital Ullevål and Faculty of Medicine University of Oslo Oslo Norway

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A multi-model identification method for the fiber stretching process based on the EM algorithm

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IFAC-PapersOnLine 2015年第8期48卷 837-842页

作者： Fan Guo Yongsheng Ding Lei Chen Lihong Ren Kuangrong Hao College of Information Science and Technology Donghua University Shanghai 201620 China Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education Shanghai 201620 China

In the fiber production process, the stretching process plays a key role in the quality of the final fiber product. Due to the fiber stretching process with inborn nonlinearity, the performance of a single controller and an optimizer may be compromised or even unsatisfactory. Thus, we consider a multi-model identification method for the fiber stretching process. The dynamic transitions among different operating points are achieved by the change of the operating conditions in the fiber stretching process. To excite all of the nonlinearity character in the fiber stretching process, the transitions among different operating conditions is achieved. The structure of each sub-models, operating points, operating range are assumed. Based on the input output data of the process, a linear parameter varying (LPV) model is built by applying a probability identification method. To achieve the smoothly connected among the different operating conditions, an exponential function is used. Then a global LPV model is constructed by synthesizing the local models. Simulated results show that the LPV method has the effectiveness in solving the inherent nonlinearity of the fiber stretching process.

关键词： LPV model Multiple model expectation maximization algorithm System identification Stretching process

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Lifetime Estimation for Multi-Phase Deteriorating Process with Random Abrupt Jumps

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SENSORS 2019年第6期19卷 1472.-1472.页

作者： Zhang, Jianxun Si, Xiaosheng Du, Dangbo Hu, Chen Hu, Changhua Xian Res Inst High Tech Dept Automat Xian 710025 Shaanxi Peoples R China Xi An Jiao Tong Univ Key Lab Educ Minist Modern Design & Rotor Bearing Syst Xian 710049 Shaanxi Peoples R China

Owing to operating condition changing, physical mutation, and sudden shocks, degradation trajectories usually exhibit multi-phase features, and the abrupt jump often appears at the changing time, which makes the traditional methods of lifetime estimation unavailable. In this paper, we mainly focus on how to estimate the lifetime of the multi-phase degradation process with abrupt jumps at the change points under the concept of the first passage time (FPT). Firstly, a multi-phase degradation model with jumps based on the Wiener process is formulated to describe the multi-phase degradation pattern. Then, we attain the lifetime's closed-form expression for the two-phase model with fixed jump relying on the distribution of the degradation state at the change point. Furthermore, we continue to investigate the lifetime estimation of the degradation process with random effect caused by unit-to-unit variability and the multi-phase degradation process. We extend the results of the two-phase case with fixed parameters to these two cases. For better implementation, a model identification method with off-line and on-line parts based on expectation maximization (EM) algorithm and Bayesian rule is proposed. Finally, a numerical case study and a practical example of gyro are provided for illustration.

关键词： life prognostics reliability multi-phase degradation expectation maximization algorithm random jump

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