Phantom Tumor Tracking in Dual-Energy Fluoroscopy using a Kalman Filter

Meyarian, Abolfazl; Namdari, Himan; Yuan, Xiaohui; Albert, Mark V.; Roeske, John C.

doi:10.1109/bibm49941.2020.9313322

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…New optimizing methods for minimizing the objective function were presented in [11], [12], [13] which could be used in minimizing the loss function. Using novel machine learning methods in healthcare and AI, there exists a good opportunity to apply the profile monitoring [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Robust Monitoring of Simple Linear Profiles Using M-estimators

Ahmadi¹,

Shahriari²

2022

CRPASE

View full text Add to dashboard Cite

In many applications of statistical process control, the quality of a product or a process is described by the relationship between the response variable and one or more independent variables which is called a profile. A profile could be either linear or nonlinear. The control limits of a chart, used to monitor a profile, are functions of model parameters. The classical estimators used to estimate the parameters, are defined under certain hypotheses such as the normality of the error terms. Deviation from any of these assumptions may cause contamination. Whenever contamination exists, the classical estimators are not robust, and the resulting control charts are not accurate when monitoring the profiles. In this research, a robust estimator of the model error term variance is introduced and evaluated using MSE. Then the robust estimators of the slope and the intercept along with the robust estimator of the error term variance are used to define the control limits for the process profile under consideration. Simulation results indicate that the out of control ARL of the proposed control charts is smaller than the ARL of the classical control charts in the presence of contamination.

show abstract

Section: Introductionmentioning

confidence: 99%