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Skip to Search Results- 10Particle filters
- 2Ensemble Kalman filter
- 2Soft Sensor
- 1Bayes Inference
- 1Bayesian State Estimation
- 1Believability
- 1Balakrishnapillai Chitralekha, Saneej
- 1Deng,Jing
- 1Hajizadeh, Mohammad
- 1Hladky, Stephen Michael
- 1Jampana, Phanindra varma
- 1Raghu, Abhinandhan
- 2Huang, Biao (Chemical and Materials Engineering)
- 1Bulitko, Vadim (Computing Science)
- 1Dr. Shah, Sirish (Chemical and Materials Engineering)
- 1Huang, Biao (Department of Chemical and Materials Engineering)
- 1Kouritzin, Mike (Mathematical and Statistical Sciences)
- 1Lipsett, Michael (Mechanical Engineering)
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Spring 2014
We introduce two kinds of particle filters, one is weighted particle filter and the other is resampling particle filter. We prove the Strong Law of Large Numbers and Central Limit Theorem for both particle filters. Then, we show that the resampling particle filter is better than the weighted one.
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Spring 2011
Balakrishnapillai Chitralekha, Saneej
The development of fast and efficient computer hardware technology has resulted in the rapid development of numerous computational software tools for making statistical inferences. The computational algorithms, which are the backbone of these tools, originate from distinct areas in science,...
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Spring 2010
The main area of research delineated in this thesis provides instances when Computer vision based technology has shown tremendous productivity gains in the Oil sands industry in Fort McMurray, Alberta, Canada. Specifically, the interface between Bitumen-froth (crude oil) and the Middlings (Sand)...
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Fault Detection and Diagnosis in Nonlinear Systems, with a Focus on Mining Truck Suspension Strut
DownloadSpring 2014
Classical fault detection methods do not completely satisfy the reliability requirement for complex and highly nonlinear stochastic systems. One solution to this problem is to use more advances fault detection methods such as multiple models to simulate system in different operating...
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Spring 2012
Limitations of measurement techniques and increasingly complex chemical process render difficulties in obtaining certain critical process variables. The hardware sensor reading may have an obvious bias compared with the real value. Off-line laboratory analysis with high accuracy can only be...
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Particle Filter for Bayesian State Estimation and Its Application to Soft Sensor Development
DownloadSpring 2012
For chemical engineering processes, state estimation plays a key role in various applications such as process monitoring, fault detection, process optimization and model based control. Thanks to their distinct advantages of inference mechanism, Bayesian state estimators have been extensively...
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Fall 2009
Commercial video game developers constantly strive to create intelligent humanoid characters that are controlled by computers. To ensure computer opponents are challenging to human players, these characters are often allowed to cheat. Although they appear skillful at playing video games,...
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RESERVOIR HISTORY MATCHING USING CONSTRAINED ENSEMBLE KALMAN FILTER AND PARTICLE FILTER METHODS
DownloadSpring 2015
The high heterogeneity of petroleum reservoirs, represented by their spatially varying rock properties (porosity and permeability), greatly dictates the quantity of recoverable oil. In this work, the estimation of these rock properties, which is crucial for the future performance prediction of a...
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Spring 2016
In this thesis, we first introduce two basic problems of filter, the nonlinear filtering and model selection problem. We show that both of them can be solved by the unnormalized filter approach. Then several web based particle filter algorithms will be discussed. We extend the resampled and...