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The mean value of gaussian distribution of excess green index: A new crop water stress indicator

AGRICULTURAL WATER MANAGEMENT 2021年 251卷 106866-106866页

作者： Zhang, Liyuan Zhang, Huihui Han, Wenting Niu, Yaxiao Chavez, Jose L. Ma, Weitong Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Colorado State Univ Dept Civil & Environm Engn Ft Collins CO 80523 USA USDA ARS Water Management & Syst Res Unit 2150 Ctr AveBldg D Ft Collins CO 80526 USA Minist Agr Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Shaanxi Peoples R China

The study proposed a new crop water stress indicator - the mean value of Gaussian distribution of excess green index for maize canopy (MGDEXG) within an RGB image. A series of RGB images were collected in a maize field under varying levels of deficit irrigation during 2013, 2015 and 2016 growth seasons in northern Colorado. To evaluate the sensitivity of MGDEXG to maize water status, canopy temperature, canopy-to-air temperature difference, crop water stress index (CWSI), leaf water potential, and sap flow were used as water status references. The results show that MGDEXG distinguished different levels of deficit irrigation treatments well and responded to the release and reimposition of deficit irrigation. The MGDEXG showed a significant correlation (p < 0.01) to different water stress references. Especially, the coefficient of determination (R2) with CWSI was 0.63 (n = 59) for 2013, 0.80 (n = 90) for 2015, and 0.80 (n = 50) for 2016. In addition, among the three Tc-based water stress indicators, the relationship between MGDEXG and CWSI was the most robust with the least annual changes of slope and intercept. The robust relationship between MGDEXG and CWSI could also show that MGDEXG was resistant to the micro-meteorological conditions within the field. Significant correlations (p < 0.01) were found between MGDEXG and leaf water potential with R2 of 0.85 and 0.87 for 2013 and 2015, and between MGDEXG and sap flow in 2015 (R2 = 0.62). MGDEXG relies only on the distribution of crop pixels within an RGB image and could be calculated easily, so it could be cheaper or easier to popularize than other crop water stress indicators in practice. Overall, our results show that MGDEXG could be successfully used as a maize water stress indicator. In the future, more field experiments are needed to further explore the changes of MGDEXG with different scale and spatial resolution of RGB images, and to evaluate MGDEXG for specific climate and crop varieties.

关键词： RGB images Canopy temperature Crop water stress index Deficit irrigation expectation-maximization algorithm

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Gaussian mixture model clustering with completed likelihood minimum message length criterion

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Journal of Southeast University(English Edition) 2013年第1期29卷 43-47页

作者：曾洪卢伟宋爱国东南大学仪器科学与工程学院南京210096 南京农业大学工学院南京210031

An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

关键词： Gaussian mixture model non-Gaussian distribution model selection expectation-maximization algorithm completed likelihood minimum message length criterion

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Multiple-model state-space system identification with time delay using the EM algorithm

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第16期361卷

作者： Gu, Ya Chen, Lin Li, Chuanjiang Yin, Shiyi Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 201418 Peoples R China

For a dynamic process identification throughout the whole operating range under diverse operating conditions, it is difficult to capture the process dynamics by a single process model in which the traditional identification method can be adopted to implement parameter estimation. By using the multiple dual-rate state-space models to approach the parameter-varying time- delay systems with different operating conditions, this paper explores an EM algorithm to simultaneously estimate the hidden variable, the parameter vector, the state variable and the time-delay by introducing hidden variables and by using a Kalman smoother.

关键词： Time delay estimation Parameter estimation expectation-maximization algorithm Kalman smoother

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Gaussian Mixture Learning for LDPC Coded BICM Receivers With Blanking Nonlinearity

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IEEE ACCESS 2019年 7卷 168720-168727页

作者： He, Yuan Jiang, Ming Zhao, Chunming Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 210096 Peoples R China

With the aid of blanking nonlinearity, the low density parity check (LDPC) coded bit-interleaved coded modulation (BICM) has been jointly considered as a robust mitigation for the impulsive interference in orthogonal frequency division multiplexing (OFDM) systems. However, the Gaussian assumption for the nonlinear channel conditional probability induces the mismatched L-values in the conventional MAP demodulator. In this paper, combined with the pulse blanking optimization via the PEXIT analysis, we propose a novel MAP demodulator based on the Gaussian mixture model (GMM) and estimate the parameters with the expectation-maximization (EM) algorithm. Taking the L-band Digital Aeronautical Communication System Type1 (L-DACS1) as an example, the GMM-based MAP demodulator can obtain the PEXIT thresholds that match the decoding curves well and provide the better BER performance in the interference-limit channel environment.

关键词： LDPC code BICM blanking nonlinearity Gaussian mixture model expectation-maximization algorithm

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Hapl-o-Mat: open-source software for HLA haplotype frequency estimation from ambiguous and heterogeneous data

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BMC BIOINFORMATICS 2017年第1期18卷 284-284页

作者： Schaefer, Christian Schmidt, Alexander H. Sauter, Juergen DKMS Gemeinnutzige GmbH Kressbach 1 D-72072 Tubingen Germany

Background: Knowledge of HLA haplotypes is helpful in many settings as disease association studies, population genetics, or hematopoietic stem cell transplantation. Regarding the recruitment of unrelated hematopoietic stem cell donors, HLA haplotype frequencies of specific populations are used to optimize both donor searches for individual patients and strategic donor registry planning. However, the estimation of haplotype frequencies from HLA genotyping data is challenged by the large amount of genotype data, the complex HLA nomenclature, and the heterogeneous and ambiguous nature of typing records. Results: To meet these challenges, we have developed the open-source software Hapl-o-Mat. It estimates haplotype frequencies from population data including an arbitrary number of loci using an expectation-maximization algorithm. Its key features are the processing of different HLA typing resolutions within a given population sample and the handling of ambiguities recorded via multiple allele codes or genotype list strings. Implemented in C++, Hapl-o-Mat facilitates efficient haplotype frequency estimation from large amounts of genotype data. We demonstrate its accuracy and performance on the basis of artificial and real genotype data. Conclusions: Hapl-o-Mat is a versatile and efficient software for HLA haplotype frequency estimation. Its capability of processing various forms of HLA genotype data allows for a straightforward haplotype frequency estimation from typing records usually found in stem cell donor registries.

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

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Diagnostic analytics for a GARCH model under skew-normal distributions

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2024年第10期53卷 4850-4874页

作者： Liu, Yonghui Wang, Jing Yao, Zhao Liu, Conan Liu, Shuangzhe Shanghai Univ Int Business & Econ Sch Stat & Informat Shanghai Peoples R China Univ New South Wales Business Sch Randwick Australia Univ Canberra Fac Sci & Technol Canberra ACT Australia

In this paper, a generalized autoregressive conditional heteroskedasticity model under skew-normal distributions is studied. A maximum likelihood approach is taken and the parameters in the model are estimated based on the expectation-maximization algorithm. The statistical diagnostics is made through the local influence technique, with the normal curvature and diagnostics results established for the model under four perturbation schemes in identifying possible influential observations. A simulation study is conducted to evaluate the performance of our proposed method and a real-world application is presented as an illustrative example.

关键词： expectation-maximization algorithm GARCH model local influence technique maximum likelihood estimation Monte Carlo simulation skew-normal distribution

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A hybrid steady-state evolutionary algorithm using random swaps for Gaussian model-based clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 208卷

作者： Kwedlo, Wojciech Bialystok Tech Univ Fac Comp Sci Wiejska 45A PL-15351 Bialystok Poland

A new hybrid evolutionary algorithm (EA) for Gaussian mixture model-based clustering is proposed. The EA is a steady-state method that, in each generation, selects two individuals from a population, creates two offspring using either mutation or crossover, and fine-tunes the offspring using the expectation maximization (EM) algorithm. The offspring compete with their parents for survival into the next generation. The approach proposed uses a random swap, which replaces a component mean with a randomly chosen feature vector as a mutation operator. In the crossover operator, a random component is copied from the source mixture into a destination mixture. Copying in crossover favors components of the source mixture located away from the components of the destination mixture. In computational experiments, the approach was compared to a multiple restarts EM algorithm, a random swap EM method, and a state-of-the-art hybrid evolutionary algorithm for Gaussian mixture model learning on one real and 29 synthetic datasets. The results indicate that, given the same computational budget, the proposed method usually learns mixtures with higher log-likelihoods than other benchmarks competing algorithms. The partitions of data obtained by the method correspond best to the original divisions of datasets into classes.

关键词： Clustering Gaussian mixture model Evolutionary algorithm expectation-maximization algorithm Hybrid optimization Random swap

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Inference of extended Weibull distribution using ramp stress accelerated life testing under censoring

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JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS 2021年第7期24卷 1445-1476页

作者： Abdel-Ghaly, Abdalla Aly, Hanan Abdel-Rahman, Elham Cairo Univ Fac Econ & Polit Sci Dept Stat Giza Egypt

In this paper, the expectation-maximization (EM) algorithm and adaptive type-II progressive censoring (A-II-PC) scheme are discussed for the first time under ramp-stress accelerated life testing (RS-ALT) experiment based on extended Weibull (EW) distribution. Under the assumptions of cumulative exposure (CE) model and inverse power law relationship, the maximum likelihood estimators (MLEs) of the unknown parameters and acceleration factors are obtained using the EM algorithm, then compared with the scoring algorithm. Furthermore, the observed information matrix based on the missing value principle is computed and used to construct the asymptotic confidence intervals for the parameters and acceleration factors. Bootstrap techniques are also considered in the interval estimation. Moreover, a numerical example is presented to illustrate the application of both the scoring and EM algorithms. Finally, a Monte Carlo simulation study is carried out to assess the performance of the suggested algorithms.

关键词： Accelerated life testing Ramp-stress Extended Weibull distribution Scoring algorithm expectation-maximization algorithm Bootstrap techniques Adaptive type-II progressive censoring

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A Study of EM algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

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Open Journal of Modelling and Simulation 2024年第2期12卷 33-42页

作者： Yisa Adeniyi Abolade Yichuan Zhao Department of Mathematics and Statistics Georgia State University Atlanta Georgia USA

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

关键词： Compositional Data Linear Regression Model Least Square Method Robust Least Square Method Synthetic Data Aitchison Distance Maximum Likelihood Estimation expectation-maximization algorithm k-Nearest Neighbor and Mean imputation

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Dynamic movement primitives based on positive and negative demonstrations

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2023年第1期20卷

作者： Dong, Shuai Yang, Zhihua Zhang, Weixi Zou, Kun Univ Elect Sci & Technol China Zhongshan Inst Zhongshan Peoples R China Univ Elect Sci & Technol China Zhongshan Inst Xueyuan Rd 1 Zhongshan 528400 Guangdong Peoples R China

Dynamic motion primitive has been the most prevalent model-based imitation learning method in the last few decades. Gaussian mixed regression dynamic motion primitive, which draws upon the strengths of both the motion model and the probability model to cope with multiple demonstrations, is a very practical and conspicuous branch in the dynamic motion primitive family. As Gaussian mixed regression dynamic motion primitive only learns from expert demonstrations and requires full environmental information, it is incapable of handling tasks with unmodeled obstacles. Aiming at this problem, we proposed the positive and negative demonstrations-based dynamic motion primitive, for which the introduction of negative demonstrations can bring additional flexibility. Positive and negative demonstrations-based dynamic motion primitive extends Gaussian mixed regression dynamic motion primitive in three aspects. The first aspect is a new maximum log-likelihood function that balances the probabilities on positive and negative demonstrations. The second one is the positive and negative demonstrations-based expectation-maximum, which involves iteratively calculating the lower bound of a new Q-function. And the last is the application framework of data set aggregation for positive and negative demonstrations-based dynamic motion primitive to handle unmodeled obstacles. Experiments on several typical robot manipulating tasks, which include letter writing, obstacle avoidance, and grasping in a grid box, are conducted to validate the performance of positive and negative demonstrations-based dynamic motion primitive.

关键词： expectation-maximization algorithm Gaussian mixed regression negative demonstrations skill transfer learning data set aggregation

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