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You searched for subject:(blind source seperation). Showing records 1 – 2 of 2 total matches.

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University of Central Florida

1. Al Rumaithi, Ayad. Characterization of Dynamic Structures Using Parametric and Non-parametric System Identification Methods.

Degree: 2014, University of Central Florida

The effects of soil-foundation-structure (SFS) interaction and extreme loading on structural behaviors are important issues in structural dynamics. System identification is an important technique to characterize linear and nonlinear dynamic structures. The identification methods are usually classified into the parametric and non-parametric approaches based on how to model dynamic systems. The objective of this study is to characterize the dynamic behaviors of two realistic civil engineering structures in SFS configuration and subjected to impact loading by comparing different parametric and non-parametric identification results. First, SFS building models were studied to investigate the effects of the foundation types on the structural behaviors under seismic excitation. Three foundation types were tested including the fixed, pile and box foundations on a hydraulic shake table, and the dynamic responses of the SFS systems were measured with the instrumented sensing devices. Parametric modal analysis methods, including NExT-ERA, DSSI, and SSI, were studied as linear identification methods whose governing equations were modeled based on linear equations of motion. NExT-ERA, DSSI, and SSI were used to analyze earthquake-induced damage effects on the global behavior of the superstructures for different foundation types. MRFM was also studied to characterize the nonlinear behavior of the superstructure during the seismic events. MRFM is a nonlinear non-parametric identification method which has advantages to characterized local nonlinear behaviors using the interstory stiffness and damping phase diagrams. The major findings from the SFS study are: *The investigated modal analysis methods identified the linearized version of the model behavior. The change of global structural behavior induced by the seismic damage could be quantified through the modal parameter identification. The foundation types also affected the identification results due to different SFS interactions. The identification accuracy was reduced as the nonlinear effects due to damage increased. *MRFM could characterize the nonlinear behavior of the interstory restoring forces. The localized damage could be quantified by measuring dissipated energy of each floor. The most severe damage in the superstructure was observed with the fixed foundation. Second, the responses of a full-scale suspension bridge in a ship-bridge collision accident were analyzed to characterize the dynamic properties of the bridge. Three parametric and non-parametric identification methods, NExT-ERA, PCA and ICA were used to process the bridge response data to evaluate the performance of mode decomposition of these methods for traffic, no-traffic, and collision loading conditions. The PCA and ICA identification results were compared with those of NExT-ERA method for different excitation, response types, system damping and sensor spatial resolution. The major findings from the ship-bridge collision study include: *PCA was able to characterize the mode shapes and modal coordinates… Advisors/Committee Members: Yun, Hae-Bum.

Subjects/Keywords: System identification; modal analysis; blind source seperation; Civil Engineering; Engineering; Geotechnical Engineering; Structural Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Al Rumaithi, A. (2014). Characterization of Dynamic Structures Using Parametric and Non-parametric System Identification Methods. (Masters Thesis). University of Central Florida. Retrieved from https://stars.library.ucf.edu/etd/1325

Chicago Manual of Style (16th Edition):

Al Rumaithi, Ayad. “Characterization of Dynamic Structures Using Parametric and Non-parametric System Identification Methods.” 2014. Masters Thesis, University of Central Florida. Accessed December 16, 2019. https://stars.library.ucf.edu/etd/1325.

MLA Handbook (7th Edition):

Al Rumaithi, Ayad. “Characterization of Dynamic Structures Using Parametric and Non-parametric System Identification Methods.” 2014. Web. 16 Dec 2019.

Vancouver:

Al Rumaithi A. Characterization of Dynamic Structures Using Parametric and Non-parametric System Identification Methods. [Internet] [Masters thesis]. University of Central Florida; 2014. [cited 2019 Dec 16]. Available from: https://stars.library.ucf.edu/etd/1325.

Council of Science Editors:

Al Rumaithi A. Characterization of Dynamic Structures Using Parametric and Non-parametric System Identification Methods. [Masters Thesis]. University of Central Florida; 2014. Available from: https://stars.library.ucf.edu/etd/1325

2. E., Okwelume Gozie. BLIND SOURCE SEPARATION USING FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS.

Degree: 2007, , Department of Signal Processing

Our thesis work focuses on Frequency-domain Blind Source Separation (BSS) in which the received mixed signals are converted into the frequency domain and Independent Component Analysis (ICA) is applied to instantaneous mixtures at each frequency bin. Computational complexity is also reduced by using this method. We also investigate the famous problem associated with Frequency-Domain Blind Source Separation using ICA referred to as the Permutation and Scaling ambiguities, using methods proposed by some researchers. This is our main target in this project; to solve the permutation and scaling ambiguities in real time applications

Gozie: [email protected] Anayo: [email protected]

Subjects/Keywords: Independent Component Analysis; Blind Source Seperation; Fourier Transform; Short Time Fourier Transform

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

E., O. G. (2007). BLIND SOURCE SEPARATION USING FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS. (Thesis). , Department of Signal Processing. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1312

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

E., Okwelume Gozie. “BLIND SOURCE SEPARATION USING FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS.” 2007. Thesis, , Department of Signal Processing. Accessed December 16, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1312.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

E., Okwelume Gozie. “BLIND SOURCE SEPARATION USING FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS.” 2007. Web. 16 Dec 2019.

Vancouver:

E. OG. BLIND SOURCE SEPARATION USING FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS. [Internet] [Thesis]. , Department of Signal Processing; 2007. [cited 2019 Dec 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1312.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

E. OG. BLIND SOURCE SEPARATION USING FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS. [Thesis]. , Department of Signal Processing; 2007. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1312

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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