scholarly journals Mixture Based Outlier Filtration

10.14311/816 ◽  
2006 ◽  
Vol 46 (2) ◽  
Author(s):  
P. Pecherková ◽  
I. Nagy
Keyword(s):  
Model Identification ◽  
Real Data ◽  
Control Algorithms ◽  
Linear Quadratic ◽  
Process Data ◽  
Linear Quadratic Gaussian ◽  
Standard Methods ◽  
Two Component ◽  
Reconstruction Filter

Success/failure of adaptive control algorithms – especially those designed using the Linear Quadratic Gaussian criterion – depends on the quality of the process data used for model identification. One of the most harmful types of process data corruptions are outliers, i.e. ‘wrong data’ lying far away from the range of real data. The presence of outliers in the data negatively affects an estimation of the dynamics of the system. This effect is magnified when the outliers are grouped into blocks. In this paper, we propose an algorithm for outlier detection and removal. It is based on modelling the corrupted data by a two-component probabilistic mixture. The first component of the mixture models uncorrupted process data, while the second models outliers. When the outlier component is detected to be active, a prediction from the uncorrupted data component is computed and used as a reconstruction of the observed data. The resulting reconstruction filter is compared to standard methods on simulated and real data. The filter exhibits excellent properties, especially in the case of blocks of outliers. 

scholarly journals Predicting gold targets using cokriging in SURFER 17

2019 ◽  
Author(s):  
Ricardo A. Valls
Keyword(s):  
Real Data ◽  
Soil Sampling ◽  
Geochemical Data ◽  
Process Data ◽  
Geochemical Model ◽  
Model Combining ◽  
Icp Ms ◽  
Initial Sampling ◽  
Extended Area

Golden Software Inc. included the method of cokriging in the newest version of SURFER 17. This has opened a new tool for interpreting geochemical data. We can use cokriging in SURFER 17 to improve the quality of maps and to predict similar targets in nearby areas. We use cokriging when we want to process data from different datasets. One dataset is always smaller than the other. Here, I first tasted the method with a hypothetical geochemical model combining a smaller dataset of FA gold results with a larger dataset of ICP-MS multi-elements. Later, I applied this method to real data from a soil sampling project in Mozambique. I tested a known mineralized target and also an extended area to predict gold targets. I also had the gold results for the extended area. They allowed me to confirm the effectiveness of cokriging in predicting the new targets. There are many opportunities where we can apply cokriging as a prediction tool. One situation is when an initial sampling returned a group of interesting but isolated gold results. We can then use a cheaper method, like ICP-MS, to better understand the gold distribution in the area.

A data-driven predictive controller combined with the vector autoregressive with exogenous input model and the propagator estimation method for vehicle lateral stabilization

2020 ◽  
Vol 235 (1) ◽  
pp. 94-116
Author(s):  
Weishun Deng ◽  
Weimiao Yang ◽  
Jianwu Zhang ◽  
Pengpeng Feng ◽  
Fan Yu
Keyword(s):  
Model Identification ◽  
Stability Control ◽  
Data Driven ◽  
Vehicle Stability ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Vector Autoregressive ◽  
Vehicle Stability Control ◽  
Predictive Controller ◽  
Input Model

A general predictive controller based on the subspace model identification method is proposed for vehicle stabilization. Traditional predictive controllers are always developed based on the principle model of vehicles, which inevitably suffers from parameter uncertainty and poor adaptability. In contrast to that, the proposed subspace-based general predictive controller is realized by a data-driven process and presents good adaptability in vehicle stability control. Inspired by subspace-based predictor construction, the keys of the predictive controller are as follows: (1) system model identification according to the model structure of the control object by input and output data; (2) output prediction of the system by the identified model; and (3) optimal control law designed by combining the linear–quadratic–Gaussian index with the predictive output. The main problem in the controller development lies in the recursive estimation of relevant matrices, which is limited by the subspace model identification theory. The implementation of the vector autoregressive with exogenous input model and the propagator method in subspace identification algorithm effectively solves the problem of estimation accuracy and calculation efficiency. Combined with a linear–quadratic–Gaussian index function, the predictive law for vehicle stability control is derived in detail. Finally, based on the vehicle model validated by standard road test, the effectiveness and robustness of the predictive controller are proved through the numerical simulations of various maneuvers under different road adhesive conditions.

Modeling and Control of a Robot Arm on a Two Wheeled Moving Platform

2015 ◽  
Vol 789-790 ◽  
pp. 735-741
Author(s):  
Mert Onkol ◽  
Coşku Kasnakoğlu
Keyword(s):  
Robot Arm ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Real Time System ◽  
Modeling And Control ◽  
Quality Of Control ◽  
Moving Platform ◽  
Linearized Model ◽  
And Control

In this paper, the derivation of dynamic model of a robot arm on a two wheeled moving platform, and design of controllers to stabilize the robot arm are presented. The modeling of two wheeled moving platform is conducted through Simmechanics® toolbox of Matlab® software. Considered control approaches are PID control and linear quadratic gaussian (LGQ) for the dynamic system. The controllers are designed by using linearized model devised from Simmechanics®. Simulation studies are discussed. Control approaches are compared in detail in terms of tracking precision, quality of control signal. The aims of this study are derivation of linearized model for designing controllers, and determining the most appropriate controller for the real time system.

Flouride and Nitrate Status of Some Ground Waters in Chittor District of Rajasthan State (India)

2018 ◽  
Vol 6 (9) ◽  
pp. 119
Author(s):  
Trapti Sharma ◽  
R. P. Nagar ◽  
R. C. Gaur ◽  
Pooja Gupta ◽  
Charanjit Kaur
Keyword(s):  
Point Of View ◽  
World Health ◽  
Indian Council ◽  
Standard Methods ◽  
Ground Waters ◽  
Drinking Waters ◽  
Health Organization ◽  
Research World ◽  
Drinking Purpose

In Rajasthan state the ground waters of some areas like Ramganj-mandi, Morak, Barmer, Jaisalmer, Chittor and Udaipur etc. are susceptible from drinking point of view.To test the quality of groundwater in Chittor district 14, ground water samples were collected from various places and analyzed for pH, E.C., Fluoride and Nitrate parameters by standard methods (A.P.H. A., Washington, USA, 1995). The study revealed that none of the ground waters was found suitable completely from drinking point of view. Some are having electrical conductivity > 1.4 dS/m, some are having pH >8.5, some area having fluoride >1.5 ppm and some are having nitrate>45 ppm. These are the limits of various parameters permitted by various International authorities like Bureau of Indian Standard, Indian Council of Medical Research,world health Organization etc. for drinking waters. So, it is recommended to the residents of above areas to use water for drinking purpose only after reverse osmosis or adopting suitable method of removing excess of Fluoride and Nitrate for drinking water to avoid unwanted pathogenic diseases harmful for human health.

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