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Weather regimes designed for local precipitation modeling: Application to the Mediterranean basin

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 2010年第D12期115卷

作者： Vrac, Mathieu Yiou, Pascal Ctr Etud Saclay Lab Sci Climat & Environm F-91191 Gif Sur Yvette France

Although weather regimes are often used as a primary step in many statistical downscaling processes, they are usually defined solely in terms of atmospheric variables and seldom to maximize their correlation to observed local meteorological phenomena. This paper compares different clustering methods to perform such a task. The correlation clustering model is introduced to define regimes that are well correlated to local-scale precipitation observed on seven French Mediterranean rain gauges. This clustering method is compared to other approaches such as the k-means and "expectation-maximization" (EM) algorithms. The two latter are applied either to the main principal components of large-scale reanalysis data(geopotential height at 500 mbar and sea level pressure) covering the Mediterranean basin or to the canonical variates associated with large scale and resulting from a canonical correlation analysis performed on reanalyses and local precipitation. The weather regimes obtained by the different approaches are compared, with a focus on the "extreme content" captured within the regimes. Then, cost functions are developed to quantify the errors due to misclassification, in terms of local precipitation. The different clustering approaches show different misclassification and costs. EM applied to canonical variates appears as a good compromise between the other approaches, with high discrimination, overall for extreme precipitation, while the precipitation costs due to bad classification are acceptable. This paper provides tools to help the users choose the clustering method to be used according to the expected goal and the use of the weather regimes.

关键词： clustering expectation-maximization algorithm downscaling

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Soft Sensor for COx Content in Tail Gas of PX Oxidation Side Reactions Based on Particle Filters and EM algorithm

Soft Sensor for COx Content in Tail Gas of PX Oxidation Side...

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2013 International Conference on Future Software Engineering and Multimedia Engineering(ICFM 2013)

作者： Ranran Liu Zhengming Li School of Electrical and Information Engineering Jiangsu University Changzhou Liu Guojun Vocational Technology College

Based on expectation-maximization algorithm, parameter estimation was proposed for data-driven nonlinear models in this work. On this basis, particle filters were used to approximately calculate integrals, deriving EM algorithm based on particle filter. And the effectiveness of using the proposed algorithm for the soft sensor of CO content in tail gas of PX oxidation side reactions was verified through simulation results.

关键词： expectation-maximization algorithm particle filtering data-driven soft sensor

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HLA Haplotype Frequency Estimation from Real-Life Data with the Hapl-o-Mat Software

HLA Haplotype Frequency Estimation from Real-Life Data with ...

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作者： Jürgen Sauter Christian Schäfer Alexander H. Schmidt

HLA haplotype frequencies are of use in a variety of settings. Such data is typically derived either from family pedigree data by targeted typing or statistical analysis of large population-specific genotype samples. As established tools for the latter approach lacked ability to treat the amount, ambiguity, and inhomogeneity found in genotype data in hematopoietic stem cell donor registries, we developed Hapl-o-Mat to alleviate these specific shortcomings. less

关键词： Statistical Calculation Genotyping Python Bioinformatics Population genetics Immunogenetics HLA Haplotype expectation-maximization algorithm Open-source software

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AN EFFICIENT ADAPTIVE ARITHMETIC CODING FOR BLOCK-BASED LOSSLESS IMAGE COMPRESSION USING MIXTURE MODELS

AN EFFICIENT ADAPTIVE ARITHMETIC CODING FOR BLOCK-BASED LOSS...

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IEEE International Conference on Image Processing

作者： Atef MASMOUDI Afif MASMOUDI William PUECH Sfax Preparatory Engineering Institute University of Sfax LIRMM UMR CNRS 5506 University of Montpellier II

ISBN: (纸本)9781479957521

In this paper, we investigate finite mixture models (FMM) and adaptive arithmetic coding (AAC) for block-based lossless image compression. The AAC performance depends on how well the model fits the source symbols' statistics. In addition, when encoding small block, the number of source symbols is considerably large by comparison with the number of samples in that block, which results in a loss of compression efficiency. To this end, we propose to model each block with an appropriately FMM by maximizing the probability of samples that belong to that block. The mixture parameters are estimated through maximum likelihood using the expectation-maximization (EM) algorithm in order to maximize the arithmetic coding efficiency. The comparative studies of some particular test images prove the efficiency of the mixture models for lossless image compression. The experimental results show significant improvements over conventional adaptive arithmetic encoders and the state-of-the-art lossless image compression standards and algorithms.

关键词： Arithmetic coding Finite mixture models Lossless image compression expectation-maximization algorithm

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Modeling temporal flow assignment in metro networks using smart card data

Modeling temporal flow assignment in metro networks using sm...

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International IEEE Conference on Intelligent Transportation Systems

作者： Lijun Sun Jian Gang Jin Future Cities Lab. Singapore-ETH Centre Singapore Singapore

ISBN: (纸本)9781467365970

Understanding passenger flow assignment patterns in a complex metro network is crucial to maintaining service reliability and developing efficient response during disruptions. In reality passengers' perception of different cost attributes may vary with time. This paper focuses on quantifying the temporal variation of passenger route choice behavior and its impact on overall passenger flow assignment. In order to efficiently estimate model parameters, we adapt a previous model to a missing data problem by introducing latent variables on route choice outcomes for each travel time observation. The revised model can be estimated using expectation-maximization (EM) algorithm. We apply the proposed framework on Singapore's metro system and temporal grouped smart card transactions. We find that route choice coefficients vary substantially with time. The relative value of transfer time in terms of in-vehicle time ranges from 2 to 3, being higher at off-peak hours than during morning/evening peaks. The result suggests that passenger care more about total travel time during peak hours, whereas comfort (e.g., less transfer time) is of more concern during off-peaks. The proposed framework is general and can be applied on other networks.

关键词： intelligent transportation systems public transport smart cards EM algorithm Singapore metro system expectation-maximization algorithm metro network passenger flow assignment patterns reliability smart card data temporal flow assignment temporal grouped smart card transaction Adaptation models Data models Estimation Reliability Smart cards Sun Tin Data-driven Flow assignment Metro network Route choice Smart card data

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Asset Life Prediction Using Multiple Degradation Indicators and Lifetime Data:a Gamma-Based State Space Model Approach

Asset Life Prediction Using Multiple Degradation Indicators ...

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The 8th International Conference on Reliability,Maintainability and Safety(ICRMS 2009)

作者： Joscph Mathcw Hack-Eun Kim Rodncy Wolff CRC of Integrated Engineering Asset Management(CIEAM) School of Engineering Systems Queensland University of Technology BrisbaneAustralia School of Mathematical Sciences Queensland University of Technology Brisbane Australia

This paper proposes a Gamma-based state space model to predict engineering asset life when multiple degradation indicators are involved and the failure threshold on these indicators are *** Carlo-based parameter estimation and model inference algorithms are developed to deal with the proposed Gamma-based state space model.A case study using real data from industry is conducted to compare the performance of the proposed model with the commonly used proportional hazard model(PHM).The result shows that the Gamma-based state space model is more appropriate to deal with the situation when the failure data is insufficient.

关键词： expectation-maximization algorithm Gamma process Proportional hazard model State space model

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A hyper-spectral Data Unsupervised Classification Method Combining Linear Projection and Gaussian Mixture Model 23

A hyper-spectral Data Unsupervised Classification Method Com...

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Proceedings of the 2023 9th International Conference on Communication and Information Processing

作者： Wenchao Li Sheng Liu Hao Ding Liang He Zhenhua Chen Qingliang Li Kaichuang Xue China Wenchang Spacescraft Launch Site China National University of Defense Technology China and China Wenchang Spacescraft Launch Site China

ISBN: (纸本)9798400708909

An unsupervised classification method combining dimensionality reduction and classification for hyper-spectral image is proposed in this *** is based on the property that lower dimensional linear projections of high dimensional data sets have the tendency to be normal, or a combination of normal distributions as the dimension *** first reduce the spectral dimensionality of the data using a principal component analysis (PCA) linear projection; then model the transformed data by a Gauss mixture models, and estimate the parameters of the model using the expectation-maximization (EM) algorithm in merge operations and select the number of components; finally we classify the data after PCA transform according to the mixture model. The experiment applying the method to Pushbroom hyper-spectral Imager(PHI) real data shows that the method is quite effective without any a prior information.

关键词： Dimensionality reduction expectation-maximization algorithm Gaussian Mixture Model Principal Component Analysis Unsupervised Classification

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An Adaptive EM algorithm for NHPP Software Reliability Models

An Adaptive EM Algorithm for NHPP Software Reliability Model...

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Reliability and Maintainability Symposium

作者： Vidhyashree Nagaraju Lance Fiondella Department of Electrical and Computer Engineering University of Massachusetts - Dartmouth

ISBN: (纸本)9781479967049

Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGM) enable several quantitative metrics that can be used to guide important decisions during the software engineering life cycle such as testing resource allocation and release planning. However, many of these SRGM possess complex mathematical forms that make them difficult to apply in practice because traditional statistical procedures such as maximum likelihood estimation must solve a system of non-linear equations to identify the numerical parameters that best characterize a set of failure data. Recently, researchers have made significant progress in overcoming this difficulty by developing an expectation-maximization (EM) algorithm that exhibits better convergence properties and can therefore find the maximum likelihood estimates of complex SRGM with greater ease. This EM algorithm, however, assumes that some model parameters are constant and thus the approach is not capable of identifying the set of numerical parameters that maximize the likelihood function.

关键词： expectation-maximization algorithm Non-homogeneous Poisson process Software reliability

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Estimating Allele Frequencies

Estimating Allele Frequencies

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作者： Indra Adrianto Courtney Montgomery

Methods of estimating allele frequencies from data on unrelated and related individuals are described in this chapter. For samples of unrelated individuals with simple codominant markers, the natural estimators of allele frequencies can be used. For genetic data on related individuals, maximum likelihood estimation (MLE) can be applied to compute allele frequencies. Factors that influence allele frequencies in populations are also explained. less

关键词： Genome-Wide Association Study DNA Sequencing Linkage Analysis Migration Mutation Population genetics Genotype Allele Phenotype Maximum likelihood estimation Hardy–Weinberg equilibrium Families Genetic drift Natural selection Pedigree Natural estimator Unrelated individuals Related individuals Relatives Founder Nonfounder Disease research Log-likelihood expectation-maximization algorithm ABO blood group Nonrandom mating

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Homography-based image mosaicing by using EM algorithm for removing inconsistent overlaid regions

Homography-based image mosaicing by using EM algorithm for r...

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SICE Annual Conference

作者： Takeaki Iiyoshi Wataru Mitsuhashi Department of Inforation and Communication Engineering University of Electro-Communications Tokyo Japan

ISBN: (纸本)9784907764302

Image mosaicing is an effective means of constructing a single panoramic image from a series of snapshots taken in different viewing angles. However, in the case of congested traffic scenes with a cluttered environment including vehicles or pedestrians, there are severe difficulties in aligning a pair of snapshots. In such cases, some objects would be taken only in one of the image pair, thereby resulting in failure in stitching the pair of images. This paper deals with three types of techniques for performing an image mosaicing: homography estimation for determining geometrical relationships between the image pair, expectation-maximization algorithm for removing inconsistent overlapping region, and Dijkstrapsilas algorithm to find the boundary for stitching the images together. Experimental results indicate that the proposed technique is effective to synthesize a panoramic image from a series of narrow-field-of-view snapshots.

关键词： Homography Image Mosaicing expectation-maximization algorithm Dijkstra's algorithm expectation-maximisation algorithm image stitching homography Snapshots panoramic image images

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