A Novel Approach for Multimodal Biometric Score Fusion Using Gaussian Mixture Model and Monte Carlo Method

2009 ◽  
Author(s):  
Raghavendra R. ◽  
Ashok Rao ◽  
Hemantha Kumar G.
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Novel Approach ◽  
Score Fusion

scholarly journals A novel approach to control the Jointed Arm Robot by tracking the position of the moving object on a conveyor using an Integrated Computer Vision system

2021 ◽  
Author(s):  
Pramod Kumar Thotapalli ◽  
◽  
CH. R. Vikram Kumar ◽  
B.ChandraMohana Reddy ◽  
◽  
...  
Keyword(s):  
Computer Vision ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Vision System ◽  
Autonomous Systems ◽  
Gaussian Mixture ◽  
Vital Role ◽  
Work Cell ◽  
Novel Approach

Computer vision algorithms play a vital role in developing self-sustained autonomous systems. The objective of the present work is to integrate the robotic system with a moving conveyor using a single camera by adopting a Gaussian Mixture Model (GMM) based background subtraction method. In this work, a simple web camera is placed above the work cell to capture the continuous images of the moving objects on the conveyor along with a jointed arm robot are connected to a microcontroller through the computer. The position of the object with time and its features are extracted from the captured image frames by subtracting its background using the Gaussian Mixture Model (GMM). The output images of GMM are further processed by image processing techniques to extract the features like shape, color, center coordinates. The extracted coordinates of objects of interest are used as input parameters to the controller to activate the base rotation of a joint arm robot to perform different manipulations. The developed algorithm is evaluated on an indigenously fabricated work cell integrated with a computer vision setup.

Numerical inversion of Gaussian mixture model for gas explosion shock wave based on a Markov chain Monte Carlo algorithm

2021 ◽  
pp. 002072092098405
Author(s):  
Jiayong Zhang ◽  
Zibo Ai ◽  
Xuemin Gong ◽  
Liwen Guo ◽  
Xiao Cui
Keyword(s):  
Shock Wave ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Em Algorithm ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Numerical Inversion ◽  
Gas Explosion

Using Markov chain Monte Carlo (MCMC) random sampling, a Gaussian mixture model (GMM) of the overpressure of a blast shock wave based on parameter optimization of an expectation-maximization (EM) algorithm is proposed to improve the accuracy of sampling. The probability of an explosion caused by gas accumulation under different conditions is obtained from statistics of gas explosion accidents. The explosion equivalent and shock wave overpressure are estimated by using field gas data. The data sets of different types of gas explosions and their corresponding density distribution functions are established. The EM algorithm is used for iterative calculation, and the optimal distribution of each gas explosion data set is obtained. The parameters are built according to a posteriori optimization. A state transition matrix is used to achieve numerical inversion of the overpressure of an MCMC gas explosion shock wave. The inversion results are based on the actual conditions of the mine. On the premise of improving the accuracy of the random simulation, the overpressure value of shock wave is more in line with the law of disaster change, which provides theoretical support for safety protection during a disaster.

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