scholarly journals Modelling the solar twin 18 Scorpii

2018 ◽  
Vol 619 ◽  
pp. A172 ◽  
Author(s):  
M. Bazot ◽  
O. Creevey ◽  
J. Christensen-Dalsgaard ◽  
J. Meléndez
Keyword(s):  
Chemical Composition ◽  
Stellar Evolution ◽  
Seismic Data ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Mixing Length ◽  
Posterior Density ◽  
Initial Chemical Composition ◽  
Probability Densities ◽  
Set Up

Context. Solar twins are objects of great interest in that they allow us to understand better how stellar evolution and structure are affected by variations of the stellar mass, age and chemical composition in the vicinity of the commonly accepted solar values. Aims. We aim to use the existing spectrophotometric, interferometric and asteroseismic data for the solar twin 18 Sco to constrain stellar evolution models. 18 Sco is the brightest solar twin and is a good benchmark for the study of solar twins. The goal is to obtain realistic estimates of its physical characteristics (mass, age, initial chemical composition, mixing-length parameter) and realistic associated uncertainties using stellar models. Methods. We set up a Bayesian model that relates the statistical properties of the data to the probability density of the stellar parameters. Special care is given to the modelling of the likelihood for the seismic data, using Gaussian mixture models. The probability densities of the stellar parameters are approximated numerically using an adaptive MCMC algorithm. From these approximate distributions we proceeded to a statistical analysis. We also performed the same exercise using local optimisation. Results. The precision on the mass is approximately 6%. The precision reached on X0 and Z0 and the mixing-length parameter are respectively 6%, 9%, and 35%. The posterior density for the age is bimodal, with modes at 4.67 Gyr and 6.95 Gyr, the first one being slightly more likely. We show that this bimodality is directly related to the structure of the seismic data. When asteroseismic data or interferometric data are excluded, we find significant losses of precision for the mass and the initial hydrogen-mass fraction. Our final estimates of the uncertainties from the Bayesian analysis are significantly larger than values inferred from local optimization. This also holds true for several estimates of the age encountered in the literature.

scholarly journals Metallicity-dependent Spectral Evolution

1999 ◽  
Vol 192 ◽  
pp. 377-380
Author(s):  
Peeter Traat
Keyword(s):  
Chemical Composition ◽  
Stellar Evolution ◽  
Spectral Evolution ◽  
Initial Chemical Composition ◽  
Radiation Output ◽  
Key Factor

The initial chemical composition of stars is, besides the mass, another key factor in stellar evolution. Through stellar lifetimes and impact on radiation output and nucleosynthesis of stars it is controlling both the pace of evolution of galactic matter/light and changes in their integrated observables and spectra.

scholarly journals Robust Spectral Features for Emotion Recognition using GMM and SVM with PCA

2019 ◽  
Vol 8 (3) ◽  
pp. 8342-8348
Keyword(s):  
General Framework ◽  
Research Work ◽  
Gaussian Mixture Models ◽  
Principal Component ◽  
Gaussian Mixture ◽  
High Dimensional ◽  
Support Vector ◽  
Speech Corpus ◽  
Vector Machines ◽  
Set Up

In this paper, the research work investigated on various spectral accents, for example, M.F.C.C, pitch-chroma, skew-ness, and centroid for feeling acknowledgment. For the test arrangement, the feelings considered in this investigation are Fear, Anger, Neutral, and Happy. The framework is assessed for different blends of spectral accents. At last, it makes sense of the blend of MFCC and skewness gave a superior acknowledgment execution when contrasted with different mixes. The previously mentioned accents are inspected utilizing Gaussian Mixture models (G.M.M.s) and Support Vector Machines (S.V.M.s). To expand the framework execution and evacuate insignificant data shape the recently produced vigorous accents, in this paper investigated an approach, namely Principal Component Analysis (PCA) is utilized to expel high dimensional information. It was set up that the acknowledgment execution for include sets in the wake of applying PCA got expanded in both grouping models utilizing GMMs and SVMs. The general framework is perceived 35% preceding PCA 58.3% later than PCA utilizing GMMs, and 28% preceding PCA, 50.5% later than PCA utilizing SVMs. The database utilized as a part of this examination is Telugu feeling speech corpus (IIT-KGP)

scholarly journals Revisiting the Hunter diagram with the Geneva Stellar Evolution Code

2014 ◽  
Vol 9 (S307) ◽  
pp. 142-143
Author(s):  
R. Simoniello ◽  
G. Meynet ◽  
S. Ekström ◽  
C. Georgy ◽  
A. Granada
Keyword(s):  
Chemical Composition ◽  
Stellar Evolution ◽  
Initial Velocity ◽  
Main Sequence ◽  
Large Magellanic Cloud ◽  
Magellanic Cloud ◽  
Main Sequence Stars ◽  
Star Evolution ◽  
Initial Chemical Composition ◽  
Stellar Masses

AbstractWe produced a model grid of rotating main and post-main sequence stars with the Geneva Stellar Evolution Code (GENEC). The initial chemical composition is tailored to compare with observations of early OB type stars in the Large Magellanic Cloud (LMC) and the grid covers stellar masses in the range of 7 ≤ M/M⊙ ≤ 15 and initial velocity between 0 km s−1 ≤ v sin(i) ≤ 300 km s−1. The model grid has been used to determine the changes in the surface Nitrogen abundances during the star evolution and the results have been compared with observations.

scholarly journals Measure of Similarity between GMMs by Embedding of the Parameter Space That Preserves KL Divergence

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 957
Author(s):  
Branislav Popović ◽  
Lenka Cepova ◽  
Robert Cep ◽  
Marko Janev ◽  
Lidija Krstanović
Keyword(s):  
Computational Complexity ◽  
Parameter Space ◽  
Recognition Accuracy ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Dimensional Manifold ◽  
Dimensional Parameter ◽  
Trade Off ◽  
Measure Of Similarity ◽  
Lower Dimensional

In this work, we deliver a novel measure of similarity between Gaussian mixture models (GMMs) by neighborhood preserving embedding (NPE) of the parameter space, that projects components of GMMs, which by our assumption lie close to lower dimensional manifold. By doing so, we obtain a transformation from the original high-dimensional parameter space, into a much lower-dimensional resulting parameter space. Therefore, resolving the distance between two GMMs is reduced to (taking the account of the corresponding weights) calculating the distance between sets of lower-dimensional Euclidean vectors. Much better trade-off between the recognition accuracy and the computational complexity is achieved in comparison to measures utilizing distances between Gaussian components evaluated in the original parameter space. The proposed measure is much more efficient in machine learning tasks that operate on large data sets, as in such tasks, the required number of overall Gaussian components is always large. Artificial, as well as real-world experiments are conducted, showing much better trade-off between recognition accuracy and computational complexity of the proposed measure, in comparison to all baseline measures of similarity between GMMs tested in this paper.

scholarly journals A Unified Formulation of k-Means, Fuzzy c-Means and Gaussian Mixture Model by the Kolmogorov–Nagumo Average

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 518
Author(s):  
Osamu Komori ◽  
Shinto Eguchi
Keyword(s):  
Pareto Distribution ◽  
Statistical Data ◽  
Learning Algorithm ◽  
Survival Function ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Simulation Studies ◽  
Probabilistic Framework ◽  
Underlying Distribution ◽  
Fuzzy C Means

Clustering is a major unsupervised learning algorithm and is widely applied in data mining and statistical data analyses. Typical examples include k-means, fuzzy c-means, and Gaussian mixture models, which are categorized into hard, soft, and model-based clusterings, respectively. We propose a new clustering, called Pareto clustering, based on the Kolmogorov–Nagumo average, which is defined by a survival function of the Pareto distribution. The proposed algorithm incorporates all the aforementioned clusterings plus maximum-entropy clustering. We introduce a probabilistic framework for the proposed method, in which the underlying distribution to give consistency is discussed. We build the minorize-maximization algorithm to estimate the parameters in Pareto clustering. We compare the performance with existing methods in simulation studies and in benchmark dataset analyses to demonstrate its highly practical utilities.

A robust non-rigid point set registration method based on inhomogeneous Gaussian mixture models

2017 ◽  
Vol 34 (10) ◽  
pp. 1399-1414 ◽  
Author(s):  
Wanxia Deng ◽  
Huanxin Zou ◽  
Fang Guo ◽  
Lin Lei ◽  
Shilin Zhou ◽  
...  
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Registration Method ◽  
Point Set Registration ◽  
Point Set

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

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