检索结果-内蒙古大学图书馆

General Meeting of the IEEE-Power-and-Energy-Society (PES)

作者： Meng, Da Zhou, Ning Lu, Shuai Lin, Guang PNNL Richland WA 99354 USA

ISBN: (纸本)9781479913039

The accuracy of a power system dynamic model is essential to its secure and efficient operation. Lower confidence in model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, this paper proposes an expectation-maximization (em) method to calibrate the synchronous machine model using phasor measurement unit (PMU) data. First, an extended Kalman filter (EKF) is applied to estimate the dynamic states using measurement data. Then, the parameters are calculated based on the estimated states using the maximum likelihood estimation (MLE) method. The em method iterates over the preceding two steps to improve estimation accuracy. The proposed em method's performance is evaluated using a single-machine infinite bus system and compared with a method where both state and parameters are estimated using an EKF method. Sensitivity studies of the parameter calibration using the em method also are presented to show the robustness of the proposed method for different levels of measurement noise and initial parameter uncertainty.

关键词： Extended Kalman Filter em algorithms State Estimation Parameter Calibration

来源：评论

学校读者我要写书评

暂无评论

An Expectation-Maximization Method for Calibrating Synchronous Machine Models

An Expectation-Maximization Method for Calibrating Synchrono...

引用

IEEE Power and Energy Society General Meeting

作者： Da Meng Ning Zhou Shuai Lu Guang Lin PNNL Richland WA

ISBN: (纸本)9781479913022

The accuracy of a power system dynamic model is essential to its secure and efficient operation. Lower confidence in model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, this paper proposes an expectation-maximization (em) method to calibrate the synchronous machine model using phasor measurement unit (PMU) data. First, an extended Kalman filter (EKF) is applied to estimate the dynamic states using measurement data. Then, the parameters are calculated based on the estimated states using the maximum likelihood estimation (MLE) method. The em method iterates over the preceding two steps to improve estimation accuracy. The proposed em method’s performance is evaluated using a single-machine infinite bus system and compared with a method where both state and parameters are estimated using an EKF method. Sensitivity studies of the parameter calibration using the em method also are presented to show the robustness of the proposed method for different levels of measurement noise and initial parameter uncertainty.

关键词： Extended Kalman Filter em algorithms State Estimation Parameter Calibration State estimation Extended Kalman filters synchronous machines expectation-maximisation algorithm single machine infinite bus system Phasor measurement units dynamic state Noise measurement maximum likelihood estimation parameter uncertainty machine model

来源：评论

学校读者我要写书评

暂无评论

A Fusion Method for Marine Radar Images Using em Algorithm

A Fusion Method for Marine Radar Images Using EM Algorithm

引用

World Automation Congress (WAC)

作者： Mita, Hokuto Kobashi, Syoji Nakagawa, Kazuya Nishiyama, Kohji Maeno, Hitoshi Kuramoto, Kei Hata, Yutaka Univ Hyogo Grad Sch Engn Himeji Initiat Computat Med & Hlth Care Technol Himeji Hyogo 6712280 Japan Osaka Univ WPI Immunol Res Ctr Suita Osaka 5650871 Japan Univ Penn Med Image Processing Grp Dept Radiol Philadelphia PA 19104 USA Furuno Elect Corp Nishinomiya Hyogo 6628580 Japan

ISBN: (纸本)9781467344975

Marine radar systems have been equipped by almost sea vehicles. However, radar image quality can be easily deteriorated by signal strength decay with increasing distance, blurring due to side lobes, signals of behind objects and of shaded area. And it is difficult to recover the artifacts. This paper proposes a new approach to improve radar image quality by image fusion. The fusion method generating high quality radar images is based on em algorithm. Performance was validated using radar images acquired from a single radar system at multiple locations. The experimental results showed that the proposed method constructed high quality radar images, and recovered signal decay and blurring due to side lobes.

关键词： component em algorithms marine radar radar image image fusion

来源：评论

学校读者我要写书评

暂无评论

Iterative Smoother-Based Variance Estimation

引用

IEEE SIGNAL PROCESSING LETTERS 2012年第5期19卷 275-278页

作者： Einicke, G. A. Falco, G. Dunn, M. T. Reid, D. C. CSIRO Pullenvale 4069 Australia ISMB I-10138 Turin Italy

The minimum-variance smoother solution for input estimation is described and it is shown that the resulting estimates are unbiased. The smoothed input and state estimates are used to iteratively identify unknown process noise variances. The use of smoothed estimates, as opposed to filtered estimates, leads to improved approximate Cramer-Rao lower bounds for the unknown parameters. It is also shown that the sequence of iterates are monotonic and asymptotically approach the actual values under prescribed conditions. A nonlinear mining navigation application is described in which unknown parameters are estimated.

关键词： em algorithms Kalman filtering smoothing

来源：评论

学校读者我要写书评

暂无评论

Improved T-Cluster Based Scheme for Combination Gene Scale Expression Data

Improved T-Cluster Based Scheme for Combination Gene Scale E...

引用

International Conference on Radar, Communication and Computing (ICRCC)

作者： Vengatesan, K. Selvarajan, S. Muthayammal Engn Coll Dept Comp Sci & Engn Namakkal Tamil Nadu India Muthayammal Tech Campus Rasipuram Namakkal Tamil Nadu India

ISBN: (纸本)9781467327589;9781467327565

Clustering is an unsupervised learning technique in that there is no explicit demarcation of data as training and test data. Clustering aims to group related records by measuring similarities among the attribute. Major phase of clustering techniques is similarity measurement and it is based on different factors and parameters. The improved Nonnegative Matrix Factorization (NMF) based TCLUST (T-Clustering) algorithm is em principle (Expectation Maximization) based algorithm, intended to search for approximate solutions. The em algorithm is the efficient method of obtaining a solution to the mixture likelihood problem. Genes with a common function are often hypothesized to have correlated expression levels across different conditions. NMF clustering is introduced to find a small number of Meta genes, each defined as a positive linear combination of the genes in the expression data. The proposed clustering algorithm is applied to a genome scale gene expression dataset to enrichment analysis and to discover highly significant biological clusters.

关键词： TCLUST Non Negative Matrix Factorization em algorithms Translation Tanimoto Correlation Coefficient

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Flow Cytometry Data Analysis

Machine Learning for Flow Cytometry Data Analysis

引用

作者： Lee, Gyemin University of Michigan

学位级别：Ph.D.

This thesis concerns the problem of automatic flow cytometry data analysis. Flow cytometry is a technique for rapid cell analysis and widely used in many biomedical and clinical laboratories. Quantitative measurements from a flow cytometer provide rich information about various physical and chemical characteristics of a large number of cells. In clinical applications, flow cytometry data is visualized on a sequence of two-dimensional scatter plots and analyzed through a manual process called "gating". This conventional analysis process requires a large amount of time and labor and is highly subjective and inefficient. In this thesis, we present novel machine learning methods for flow cytometry data analysis to address these issues. We first begin by a method for generating a high dimensional flow cytometry dataset from multiple low dimensional datasets. We present an imputation algorithm based on clustering and show that it improves upon a simple nearest neighbor based approach that often induces spurious clusters in the imputed data. This technique enables the analysis of multi-dimensional flow cytometry data beyond the fundamental measurement limits of instruments. We then present two machine learning methods for automatic gating problems. Gating is a process of identifying interesting subsets of cell populations. Pathologists make clinical decisions by inspecting the results from gating. Unfortunately, this process is performed manually in most clinical settings and poses many challenges in high-throughput analysis. The first approach is an unsupervised learning technique based on multivariate mixture models. Since measurements from a flow cytometer are often censored and truncated, standard model-fitting algorithms can cause biases and lead to poor gating results. We propose novel algorithms for fitting multivariate Gaussian mixture models to data that is truncated, censored, or truncated and censored. Our second approach is a transfer learning technique combine

关键词： Machine Learning Flow Cytometry Support Vector Machine Mixture Models em algorithms Statistical File Matching Thesis

来源：评论

学校读者我要写书评

暂无评论

Understanding the variability of speed distributions under mixed traffic conditions caused by holiday traffic

引用

TRANSPORTATION RESEARCH PART C-emERGING TECHNOLOGIES 2010年第4期18卷 599-610页

作者： Jun, Jungwook Virginia Dept Transportat Traff Engn Div Richmond VA 23219 USA

Understanding the variability of speed patterns and congestion characteristics of interstate freeway systems caused by holiday traffic is beneficial because appropriate countermeasures for safety improvement and congestion mitigation can be prepared and drivers can avoid traffic congestion and change their holiday travel schedules. This study evaluated the traffic congestion patterns during the Thanksgiving holiday period in 2006 using a Gaussian mixture speed distribution estimated by the Expectation-Maximization (em) algorithm. This mathematical approach showed the potential of improving freeway operational performance evaluation schemes for holiday periods (even non-holiday periods). This study suggested that a Gaussian mixture model using the em algorithm could be used to properly characterize the severity and the variability of congestion on certain interstate roadway systems. However, this study also pointed out that the fundamental limitations of the mixture model and the statistical significance test about the mixture components should be well understood and need to be further investigated. In addition, because this study investigated the changing patterns of speed distributions with only one interstate freeway system, 1-95 northbound, other freeway systems with both directions need to be evaluated so that a more broad and confident analysis on holiday traffic can be achieved. Published by Elsevier Ltd.

关键词： Speed distributions Gaussian mixture models em algorithms Holiday traffic Congestions Bimodal distributions

来源：评论

学校读者我要写书评

暂无评论

Mixtures of regressions with predictor-dependent mixing proportions

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2010年第10期54卷 2253-2266页

作者： Young, D. S. Hunter, D. R. Penn State Univ Dept Stat University Pk PA 16802 USA

We extend the standard mixture of linear regressions model by allowing the mixing proportions to be modeled nonparametrically as a function of the predictors. This framework allows for more flexibility in the modeling of the mixing proportions than the fully parametric mixture of experts model, which we also discuss. We present an em-like algorithm for estimation of the new model. We also provide simulations demonstrating that our nonparametric approach can provide a better fit than the parametric approach in some instances and can serve to validate and thus reinforce the parametric approach ill others. We also analyze and interpret two real data sets using the new method. (C) 2010 Elsevier B.V. All rights reserved.

关键词： em algorithms Hierarchical mixture of experts Mixture models Mixtures of regressions

来源：评论

学校读者我要写书评

暂无评论

State Estimation Schemes for Independent Component Coupled Hidden Markov Models

引用

STOCHASTIC ANALYSIS AND APPLICATIONS 2010年第3期28卷 430-446页

作者： Malcolm, W. P. Quadrianto, Novi Aggoun, Lakhdar Australian Natl Univ Inst Math Sci Canberra ACT 0200 Australia Natl Informat Commun Technol Ctr Australia NICTA Canberra ACT Australia Sultan Qaboos Univ Dept Math & Stat Muscat Oman

Conventional Hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive noise. This general class of model has enjoyed a huge and diverse range of applications, for example, speech processing, biomedical signal processing and more recently quantitative finance. However, a lesser known extension of this general class of model is the so-called Factorial Hidden Markov Model (FHMM). FHMMs also have diverse applications, notably in machine learning, artificial intelligence and speech recognition [13, 17]. FHMMs extend the usual class of HMMs, by supposing the partially observed state process is a finite collection of distinct Markov chains, either statistically independent or dependent. There is also considerable current activity in applying collections of partially observed Markov chains to complex action recognition problems, see, for example, [6]. In this article we consider the Maximum Likelihood (ML) parameter estimation problem for FHMMs. Much of the extant literature concerning this problem presents parameter estimation schemes based on full data log-likelihood em algorithms. This approach can be slow to converge and often imposes heavy demands on computer memory. The latter point is particularly relevant for the class of FHMMs where state space dimensions are relatively large. The contribution in this article is to develop new recursive formulae for a filter-based em algorithm that can be implemented online. Our new formulae are equivalent ML estimators, however, these formulae are purely recursive and so, significantly reduce numerical complexity and memory requirements. A computer simulation is included to demonstrate the performance of our results.

关键词： Change of measure techniques em algorithms Factorial hidden Markov models Filtering Martingales M-ary detection Parameter estimation Smoothing

来源：评论

学校读者我要写书评

暂无评论

New estimation and feature selection methods in mixture-of-experts models

引用

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 2010年第4期38卷 519-539页

作者： Khalili, Abbas McGill Univ Dept Math & Stat Montreal PQ H3A 2K6 Canada

We study estimation and feature selection problems in mixture of-experts models An l(2) penalized maximum likelihood estimator is proposed as an alternative to the ordinary maximum likelihood estimator The estimator is particularly advantageous when fitting a mixture-of experts model to data with many correlated features It is shown that the proposed estimator is root n consistent and simulations show its superior finite sample behaviour compared to that of the maximum likelihood estimator For feature selection two extra penalty functions are applied to the l(2)-penalized log likelihood function The proposed feature selection method is computationally much more efficient than the popular all-subset selection methods Theoretically it is shown that the method is consistent in feature selection and simulations support our theoretical results A real-data example is presented to demonstrate the method The Canadian Journal of Statistics 38 519-539 2010 (C) 2010 Statistical Society of Canada

关键词： Mixture-of experts regularization LASSO SCAD em algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：