Probability density function of ocean noise based on a variational Bayesian Gaussian mixture model

2020 ◽  
Vol 147 (4) ◽  
pp. 2087-2097
Author(s):  
Ying Zhang ◽  
Kunde Yang ◽  
Qiulong Yang
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Variational Bayesian ◽  
Ocean Noise

scholarly journals On the Uncertainty Identification for Linear Dynamic Systems Using Stochastic Embedding Approach with Gaussian Mixture Models

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3837
Author(s):  
Rafael Orellana ◽  
Rodrigo Carvajal ◽  
Pedro Escárate ◽  
Juan C. Agüero
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Dynamic Systems ◽  
Gaussian Mixture ◽  
Error Model ◽  
Fault Detection And Diagnosis ◽  
Deterministic Models ◽  
Linear Dynamic Systems ◽  
Linear Dynamic

In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and able to describe the entire dynamics of an aircraft. In addition, the complexity of modern real systems has turned deterministic models impractical, since they cannot adequately represent the behavior of disturbances in sensors and actuators, and tool and machine wear, to name a few. Thus, it is necessary to deal with model uncertainties in the dynamics of the plant by incorporating a stochastic behavior. These uncertainties could also affect the effectiveness of fault diagnosis methodologies used to increment the safety and reliability in real-world systems. Determining suitable dynamic system models of real processes is essential to obtain effective process control strategies and accurate fault detection and diagnosis methodologies that deliver good performance. In this paper, a maximum likelihood estimation algorithm for the uncertainty modeling in linear dynamic systems is developed utilizing a stochastic embedding approach. In this approach, system uncertainties are accounted for as a stochastic error term in a transfer function. In this paper, we model the error-model probability density function as a finite Gaussian mixture model. For the estimation of the nominal model and the probability density function of the parameters of the error-model, we develop an iterative algorithm based on the Expectation-Maximization algorithm using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.

scholarly journals Adaptive Weight Update Algorithm for Target Tracking of UUV Based on Improved Gaussian Mixture Cubature Kalman Filter

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hongjian Wang ◽  
Ying Wang ◽  
Cun Li ◽  
Juan Li ◽  
Qing Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Probability Density Function ◽  
Target Tracking ◽  
Probability Density ◽  
Density Function ◽  
Gaussian Mixture ◽  
System State ◽  
Cubature Kalman Filter ◽  
Non Gaussian ◽  
Time Update

The Gaussian mixture filter can solve the non-Gaussian problem of target tracking in complex environment by the multimode approximation method, but the weights of the Gaussian component of the conventional Gaussian mixture filter are only updated with the arrival of the measurement value in the measurement update stage. When the nonlinear degree of the system is high or the measurement value is missing, the weight of the Gauss component remains unchanged, and the probability density function of the system state cannot be accurately approximated. To solve this problem, this paper proposes an algorithm to update adaptive weights for the Gaussian components of a Gaussian mixture cubature Kalman filter (CKF) in the time update stage. The proposed method approximates the non-Gaussian noise by splitting the system state, process noise, and observation noise into several Gaussian components and updates the weight of the Gaussian components in the time update stage. The method contributes to obtaining a better approximation of the posterior probability density function, which is constrained by the substantial uncertainty associated with the measurements or ambiguity in the model. The estimation accuracy of the proposed algorithm was analyzed using a Taylor expansion. A series of extensive trials was performed to assess the estimation precision corresponding to various algorithms. The results based on the data pertaining to the lake trial of an unmanned underwater vehicle (UUV) demonstrated the superiority of the proposed algorithm in terms of its better accuracy and stability compared to those of conventional tracking algorithms, along with the associated reasonable computational time that could satisfy real-time tracking requirements.

Semi-Parametric Learning for Classification of Pitting Corrosion Detected by Acoustic Emission

2006 ◽  
Vol 321-323 ◽  
pp. 549-552 ◽  
Author(s):  
Asa Prateepasen ◽  
Pakorn Kaewtrakulpong ◽  
Chalermkiat Jirarungsatean
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Mixture Model ◽  
Pitting Corrosion ◽  
Probabilistic Neural Network ◽  
Conditional Probability Density ◽  
Statistical Decision Theory

This paper presents a Non-Destructive Testing (NDT) technique, Acoustic Emission (AE) to classify pitting corrosion severity in austenitic stainless steel 304 (SS304). The corrosion severity is graded roughly into five levels based on the depth of corrosion. A number of timedomain AE parameters were extracted and used as features in our classification methods. In this work, we present practical classification techniques based on Bayesian Statistical Decision Theory, namely Maximum A Posteriori (MAP) and Maximum Likelihood (ML) classifiers. Mixture of Gaussian distributions is used as the class-conditional probability density function for the classifiers. The mixture model has several appealing attributes such as the ability to model any probability density function (pdf) with any precision and the efficiency of parameter-estimation algorithm. However, the model still suffers from model-order-selection and initialization problems which greatly limit its applications. In this work, we introduced a semi-parametric scheme for learning the mixture model which can solve the mentioned difficulties. The method was compared with conventional Feed-Forward Neural Network (FFNN) and Probabilistic Neural Network (PNN) to evaluate its performance. We found that our proposed methods gave much lower classificationerror rate and also far smaller variance of the classifiers.

Background Subtraction Based on Gaussian Mixture Model

2013 ◽  
Vol 694-697 ◽  
pp. 2021-2026
Author(s):  
De Fang Liu ◽  
Ming Deng ◽  
Dai Mu Wang
Keyword(s):  
Probability Density ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Adaptive Algorithm ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Video Sequences ◽  
Recursive Equations ◽  
Gauss Mixture

According to the detection of moving objects in video sequences, the paper puts forward background subtraction based on Gauss mixture model. It analyzes the usual pixel-level approach, and to develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.

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