Gaussian Mixed Model Research Articles

A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems

Context information is rarely used in the object-based landcover classification. Previous models that attempted to utilize this information usually required the user to input empirical values for critical model parameters, leading to less optimal performance. Multi-view image information is useful for improving classification accuracy, but the methods to assimilate multi-view information to make it usable for context driven models have not been explored in the literature. Here we propose a novel method to exploit the multi-view information for generating class membership probability. Moreover, we develop a new conditional random field model to integrate multi-view information and context information to further improve landcover classification accuracy. This model does not require the user to manually input parameters because all parameters in the Conditional Random Field (CRF) model are fully learned from the training dataset using the gradient descent approach. Using multi-view data extracted from small Unmanned Aerial Systems (UASs), we experimented with Gaussian Mixed Model (GMM), Random Forest (RF), Support Vector Machine (SVM) and Deep Convolutional Neural Networks (DCNN) classifiers to test model performance. The results showed that our model improved average overall accuracies from 58.3% to 74.7% for the GMM classifier, 75.8% to 87.3% for the RF classifier, 75.0% to 84.4% for the SVM classifier and 80.3% to 86.3% for the DCNN classifier. Although the degree of improvement may depend on the specific classifier respectively, the proposed model can significantly improve classification accuracy irrespective of classifier type.

INFRARED ROTATION PLANE RADAR CHANGE DETECTION

Abstract. The existing change detection method mainly stays on the pixel-level, which is very susceptible to light, shadow, etc. And the complex calculation and analysis for each pixel reduce the detection efficiency. Moreover, there is no modeling determination method to initialize standard deviation of each element for existing mixed Gaussian background modeling methods. In this paper, an improved mixed Gaussian background modeling method is proposed, with the use of infrared rotation plane radar. The relationship between the corrected standard deviation of distance and the detection intensity is used to establish the curve of standard deviation of distance with detected intensity. For each data point, the standard deviation is initialized by the value estimated by the change curve, and the detected distance is used to establish the Gaussian mixture background model. The detection effect of the method is discussed and compared with the traditional Gauss background modeling in the experiment, the result shows that it has certain advantages in processing speed, adaptability to change background and accuracy of change detection.

A THRESHOLD-FREE FILTERING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUDS BASED ON EXPECTATION-MAXIMIZATION

Abstract. Filtering is a key step for most applications of airborne LiDAR point clouds. Although lots of filtering algorithms have been put forward in recent years, most of them suffer from parameters setting or thresholds adjusting, which will be time-consuming and reduce the degree of automation of the algorithm. To overcome this problem, this paper proposed a threshold-free filtering algorithm based on expectation-maximization. The proposed algorithm is developed based on an assumption that point clouds are seen as a mixture of Gaussian models. The separation of ground points and non-ground points from point clouds can be replaced as a separation of a mixed Gaussian model. Expectation-maximization (EM) is applied for realizing the separation. EM is used to calculate maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or object can be computed. After several iterations, point clouds can be labelled as the component with a larger likelihood. Furthermore, intensity information was also utilized to optimize the filtering results acquired using the EM method. The proposed algorithm was tested using two different datasets used in practice. Experimental results showed that the proposed method can filter non-ground points effectively. To quantitatively evaluate the proposed method, this paper adopted the dataset provided by the ISPRS for the test. The proposed algorithm can obtain a 4.48 % total error which is much lower than most of the eight classical filtering algorithms reported by the ISPRS.

OLED Panel Radiation Pattern and Its Impact on VLC Channel Characteristics

Organic light-emitting diodes (OLEDs) show several attractive features superior to traditional semiconductor LEDs for visible light communication (VLC). This paper experimentally tests and models the radiation pattern of a bent OLED panel for the first time. Different from a Lambertian radiation pattern, we propose an improved analytic mixed Gaussian model to describe the rotational radiation asymmetry, whose parameter values are found by applying an expectation-maximization algorithm for curve fitting with measurements. The OLED radiation pattern is observed to show strong directionality along the bent axes. Its impact on VLC channel characteristics is further studied. Compared with the Lambertian source, the OLED source is more flexible in radiation pattern control and shows advantages in smaller root mean square delay spread and optical path loss.

MerDeriv: Derivative Computations for Linear Mixed Effects Models with Application to Robust Standard Errors

While robust standard errors and related facilities are available in R for many types of statistical models, the facilities are notably lacking for models estimated via lme4. This is because the necessary statistical output, including the Hessian and casewise gradient of random effect parameters, is not immediately available from lme4 and is not trivial to obtain. In this article, we supply and describe two new functions to obtain this output from Gaussian mixed models: estfun.lmerMod() and vcov.full.lmerMod(). We discuss the theoretical results implemented in the code, focusing on calculation of robust standard errors via package sandwich. We also use the Sleepstudy data to illustrate the code and compare it to a benchmark from package lavaan.

Multiple Response Variables Regression Models in R: The mcglm Package

This article describes the R package mcglm implemented for fitting multivariate covariance generalized linear models (McGLMs). McGLMs provide a general statistical modeling framework for normal and non-normal multivariate data analysis, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function and a matrix linear predictor involving known symmetric matrices. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modeled by means of a link function and a linear predictor. The models are fitted using an estimating function approach based on second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, genetic, spatial and spatio-temporal structures. The mcglm package allows a flexible specification of the mean and covariance structures, and explicitly deals with multivariate response variables, through a user friendly formula interface similar to the ordinary glm function. Illustrations in this article cover a wide range of applications from the traditional one response variable Gaussian mixed models to multivariate spatial models for areal data using the multivariate Tweedie distribution. Additional features, such as robust and bias-corrected standard errors for regression parameters, residual analysis, measures of goodness-of-fit and model selection using the score information criterion are discussed through six worked examples. The mcglm package is a full R implementation based on the Matrix package which provides efficient access to BLAS (basic linear algebra subroutines), Lapack (dense matrix), TAUCS (sparse matrix) and UMFPACK (sparse matrix) routines for efficient linear algebra in R.

2- and 3-D Urban Change Detection With Quad-PolSAR Data

In this letter, an unsupervised 2-D and 3-D urban change detection scheme is proposed exploiting Quad-PolSAR data. Changes are extracted by segmenting the data into superpixels, to enhance the balance among change components and increase estimability of prior distributions. Positive and negative change components for built-up areas, in both the horizontal and the vertical directions, are properly extracted by assuming a multivariate Gaussian mixed model applied to a subset of polarimetric parameters at the superpixel level. The proposed method is tested on multitemporal Quad-PolSAR images and the results confirm its effectiveness. The selection of polarimetric decomposition measures that are most useful to the task is also experimentally justified.

An Integrated Approach for Design Improvement Based on Analysis of Time-Dependent Product Usage Data

With the recent advances in information gathering techniques, product performances and environment/operation conditions can be monitored, and product usage data, including time-dependent product performance feature data and field data (i.e., environmental/operational data), can be continuously collected during the product usage stage. These technologies provide opportunities to improve product design considering product functional performance degradation. The challenge lies in how to assess data of product functional performance degradation for identifying relevant field factors and changing design parameters. An integrated approach for design improvement is developed in this research to transform time-dependent usage data to design information. Many data modeling and analysis techniques such as hierarchal function model, performance feature dimension reduction method, Gaussian mixed model (GMM), and data clustering method are employed in this approach. These methods are used to extract principal features from collected performance features, assess product functional performance degradation, and group field data into meaningful data clusters. The abnormal field data causing severe and rapid product function degradation are obtained based on the field data clusters. A redesign necessity index (RNI) is defined for each design parameter related to severely degraded functions based on the relationships between this design parameter and abnormal field data. An associate relationship matrix (ARM) is constructed to calculate the RNI of each design parameter for identifying the to-be-modified design parameters with high priorities for product improvement. The effectiveness of this new approach is demonstrated through a case study for the redesign of a large tonnage crawler crane.

Comparing Fuzzy Sets and Random Sets to Model the Uncertainty of Fuzzy Shorelines

This paper addresses uncertainty modelling of shorelines by comparing fuzzy sets and random sets. Both methods quantify extensional uncertainty of shorelines extracted from remote sensing images. Two datasets were tested: pan-sharpened Pleiades with four bands (Pleiades) and pan-sharpened Pleiades stacked with elevation data as the fifth band (Pleiades + DTM). Both fuzzy sets and random sets model the spatial extent of shoreline including its uncertainty. Fuzzy sets represent shorelines as a margin determined by upper and lower thresholds and their uncertainty as confusion indices. They do not consider randomness. Random sets fit the mixed Gaussian model to the image histogram. It represents shorelines as a transition zone between water and non-water. Their extensional uncertainty is assessed by the covering function. The results show that fuzzy sets and random sets resulted in shorelines that were closely similar. Kappa (κ) values were slightly different and McNemar’s test showed high p-values indicating a similar accuracy. Inclusion of the DTM (digital terrain model) improved the classification results, especially for roofs, inundated houses and inundated land. The shoreline model using Pleiades + DTM performed better than that of using Pleiades only, when using either fuzzy sets or random sets. It achieved κ values above 80%.

Gaussian mixed model in support of semiglobal matching leveraged by ground control points

Semiglobal matching (SGM) has been widely applied in large aerial images because of its good tradeoff between complexity and robustness. The concept of ground control points (GCPs) is adopted to make SGM more robust. We model the effect of GCPs as two data terms for stereo matching between high-resolution aerial epipolar images in an iterative scheme. One term based on GCPs is formulated by Gaussian mixture model, which strengths the relation between GCPs and the pixels to be estimated and encodes some degree of consistency between them with respect to disparity values. Another term depends on pixel-wise confidence, and we further design a confidence updating equation based on three rules. With this confidence-based term, the assignment of disparity can be heuristically selected among disparity search ranges during the iteration process. Several iterations are sufficient to bring out satisfactory results according to our experiments. Experimental results validate that the proposed method outperforms surface reconstruction, which is a representative variant of SGM and behaves excellently on aerial images.

Genome-wide association study in an F2 Duroc x Pietrain resource population for economically important meat quality and carcass traits.

Meat quality is essential for consumer acceptance, it ultimately impacts pork production profitability and it is subject to genetic control. The objective of this study was to map genomic regions associated with economically important meat quality and carcass traits. We performed a genome-wide association (GWA) analysis to map regions associated with 38 meat quality and carcass traits recorded for 948 F2 pigs from the Michigan State University Duroc × Pietrain resource population. The F0, F1, and 336 F2 pigs were genotyped with the Illumina Porcine SNP60 BeadChip, while the remaining F2 pigs were genotyped with the GeneSeek Genomic Profiler for Porcine Low Desnisty (LD) chip, and imputed with high accuracy ( = 0.97). Altogether the genomic dataset comprised 1,019 animals and 44,911 SNP. A Gaussian linear mixed model was fitted to estimate the breeding values and the variance components. A linear transformation was performed to estimate the marker effects and variances. Type I error rate was controlled at a False Discovery Rate of 5%. Seven putative QTL found in this study were previously reported in other studies. Two novel QTL associated with tenderness (TEN) were located on SSC3 [135.6:137.5Mb; False Discovery rate (FDR) < 0.03] and SSC5 (67.3:69.1Mb; FDR < 0.02). The QTL region identified on SSC15 includes Protein Kinase AMP-activated ɣ 3-subunit gene (), which has been associated with 24-h pH (pH24), drip loss (DL) and cook yield (CY). Also, novel candidate genes were identified for TEN in the region on SSC5 [A Kinase (PRKA) Anchor Protein 3 (], and for tenth rib backfat thickness (BF10) [Carnitine O-Acetyltransferase ()] on SSC1. The association of gene polymorphisms with pork quality traits has been reported for several pig populations. However, there are no SNP for this gene on the chip used, thus we genotyped the animals for 2 non-synonymous variants ( and ). We then performed a GWA conditioning on the genotype of both SNP and was associated with pH24, DL, protein content (PRO) and CY ( < 0.004) and T30N with Juiciness, TEN, shear force, pH24, PRO, and CY < 0.04). Finally, we performed a GWA conditioning on the genotype of the SNP peak detected in this study, and T30N remained associated only with PRO ( < 0.02). Therefore, in this study we identified 2 novel QTL regions, suggest 2 novel candidate genes, and conclude that other SNP in PRKAG3 or nearby gene(s) explain the observed associations on SSC15 in this population.

Receiver design for SPAD-based VLC systems under Poisson-Gaussian mixed noise model.

Single-photon avalanche diode (SPAD) is a promising photosensor because of its high sensitivity to optical signals in weak illuminance environment. Recently, it has drawn much attention from researchers in visible light communications (VLC). However, existing literature only deals with the simplified channel model, which only considers the effects of Poisson noise introduced by SPAD, but neglects other noise sources. Specifically, when an analog SPAD detector is applied, there exists Gaussian thermal noise generated by the transimpedance amplifier (TIA) and the digital-to-analog converter (D/A). Therefore, in this paper, we propose an SPAD-based VLC system with pulse-amplitude-modulation (PAM) under Poisson-Gaussian mixed noise model, where Gaussian-distributed thermal noise at the receiver is also investigated. The closed-form conditional likelihood of received signals is derived using the Laplace transform and the saddle-point approximation method, and the corresponding quasi-maximum-likelihood (quasi-ML) detector is proposed. Furthermore, the Poisson-Gaussian-distributed signals are converted to Gaussian variables with the aid of the generalized Anscombe transform (GAT), leading to an equivalent additive white Gaussian noise (AWGN) channel, and a hard-decision-based detector is invoked. Simulation results demonstrate that, the proposed GAT-based detector can reduce the computational complexity with marginal performance loss compared with the proposed quasi-ML detector, and both detectors are capable of accurately demodulating the SPAD-based PAM signals.

Tracking a Moving Objects Using Foreground Detector and Improved Morphological Filter

Mobile object detection is one of the most important steps in computer vision applications such as: medical analysis human-machine interface, robotics, traffic monitoring, and more. In this article, we apply the Gaussian mixing model which is established on the background subtraction. A smoothing method was used for the pre-processing step and a morphological filter was applied to remove unwanted pixels from the background in the other to solve the problem of background noise disturbance. We also demonstrated that filtering foreground segmentation twice with the same morphological structured element but with a different width was used to improve the accuracy of the result. The results show that the proposed method is effective in detecting and tracking moving vehicles, compared to filter segmentation in the foreground only once. Several methods and algorithms have been used to solve this problem. All the methods used before have been effective but also have limits. Some of these methods lose the object when the number of frames is wide while others lose it when it changes direction or rolls at a high speed. In addition, the algorithms proposed for the detection of colors in RGB also lose their objectives when the object changes the color. But the proposed combination in this paper maintains contact with the object without losing it even if it changes direction or speed or the number of frame increases.

Heterogeneous platoon flow dispersion model based on truncated mixed simplified phase‐type distribution of travel speed

SummaryExisting research on platoon dispersion models either describe homogeneous traffic flow feature, or are in lack of analytical solutions. By analyzing the field data, the truncated mixed simplified phase‐type distribution is proved to be capable of capturing the characteristics of heterogeneous traffic flow with an excellent fitting result. In light of this, we derive a generic heterogeneous platoon dispersion model with truncated mixed simplified phase‐type of speed in the forms of integrable functions. Numerical case studies are conducted to compare the performance of the proposed model and the conventional models (i.e., the Robertson model and truncated mixed Gaussian model). The results show that the proposed model not only better captures the platoon dispersion laws of heterogeneous traffic flow, but also presents higher computational efficiency, which provides practical implications on traffic signal control. Copyright © 2017 John Wiley &amp; Sons, Ltd.

&lt;b&gt;Mixed models in cerebral ischemia study

The data modeling from longitudinal studies stands out in the current scientific scenario, especially in the areas of health and biological sciences, which induces a correlation between measurements for the same observed unit. Thus, the modeling of the intra-individual dependency is required through the choice of a covariance structure that is able to receive and accommodate the sample variability. However, the lack of methodology for correlated data analysis may result in an increased occurrence of type I or type II errors and underestimate/overestimate the standard errors of the model estimates. In the present study, a Gaussian mixed model was adopted for the variable response latency of an experiment investigating the memory deficits in animals subjected to cerebral ischemia when treated with fish oil (FO). The model parameters estimation was based on maximum likelihood methods. Based on the restricted likelihood ratio test and information criteria, the autoregressive covariance matrix was adopted for errors. The diagnostic analyses for the model were satisfactory, since basic assumptions and results obtained corroborate with biological evidence; that is, the effectiveness of the FO treatment to alleviate the cognitive effects caused by cerebral ischemia was found.

Timely online chatter detection in end milling process

Chatter is one of the most unexpected and uncontrollable phenomenon during the milling operation. It is very important to develop an effective monitoring method to identify the chatter as soon as possible, while existing methods still cannot detect it before the workpiece has been damaged. This paper proposes an energy aggregation characteristic-based Hilbert–Huang transform method for online chatter detection. The measured vibration signal is firstly decomposed into a series of intrinsic mode functions (IMFs) using ensemble empirical mode decomposition. Feature IMFs are then selected according to the majority energy rule. Subsequently Hilbert spectral analysis is applied on these feature IMFs to calculate the Hilbert time/frequency spectrum. Two indicators are proposed to quantify the spectrum and thresholds are automatically calculated using Gaussian mixed model. Milling experiments prove the proposed method to be effective in protecting the workpiece from severe chatter damage within acceptable time complexity.

A hybrid hidden Markov model towards fault detection of rotating components

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

Using an improved BEMD method to analyse the characteristic scale of aeromagnetic data in the Gejiu region of Yunnan, China

The geological and metallogenic process is a typical non-stationary multifactor and multi-scale random process. Multiple measurement data assess the performance of the integrated process, and the combined data set is usually large and complex, among other characteristics. When different metallogenic prediction targets exist, the data must be decomposed on different scales in space. The study of the scale interval in which the object features are located can eliminate useless information and retrieve useful scale data that are needed for metallogenic prediction. Thus, the model that the specific deposit presents will be rapidly and accurately identified to enhance the efficiency of the prediction and analysis models. This paper employs an improved bidimensional empirical decomposition method to decompose aeromagnetic survey data and expresses and decomposes the spatial distribution of deposits with a mixed Gaussian model. By comparing the decomposition results on various sampling data scales with the distribution function for the deposit, the characteristic scale interval that contains the measurement information that exhibits the greatest similarity to the distribution of the deposits can be identified. This method was employed to analyse a Yunnan Gejiu tin–copper polymetallic deposit using aeromagnetic sampling data to calculate suitable decomposition-scale parameters. This approach provides valuable parameters for metallogenic prediction in other areas with aeromagnetic data.

Bird Species Classification Based on Mixed-GMM

A bird species classification method is raised on the basis of Mel-Frequency Cepstral Coefficients (MFCC) and mixed Gaussian Mixture Model (mixed-GMM). The mixed-GMM is established according to the species of birds by classifying each bird recordings into call-song model (CSM) and male-female model (MFM), and extracting each species’ feature parameter respectively. In the recognition phase, each unknown bird recording’ MFCC is matching with the known CSM and MFM, and to classify it into the species which gets the highest score with its model. Results show that the accuracy rate of mixed-GMM is higher than conventional GMM.

Statistical monitoring of over-dispersed multivariate count data using approximate likelihood ratio tests

In this paper, we develop a statistical monitoring scheme for multivariate count data. In many applications involving multivariate count data, individual variables are not only correlated to each other, but also over-dispersed. Traditional statistical monitoring methods for multivariate count data that assume simple statistical models fail to fit the data collected when the underlying process is under normal working state, also referred to as the in-control state. Therefore, we propose a monitoring scheme which is based on the Poisson–multivariate Gaussian mixed model. Although such models are quite flexible, efficient statistical monitoring schemes for such models have not been developed. In this paper, we develop likelihood ratio test-based monitoring schemes that are shown to be superior to standard multivariate statistical monitoring schemes. The key challenge in developing likelihood ratio test for the Poisson–multivariate Gaussian mixed models is that the likelihood function can only be calculated by multidimensional numerical integration. We tackle this issue using an approximation of this complex likelihood function.