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You searched for +publisher:"Georgia Tech" +contributor:("Rogers, Jonathan D."). Showing records 1 – 2 of 2 total matches.

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1. Fowler, Lee Everett. A Virtual pilot algorithm for synthetic HUMS data generation.

Degree: MS, Mechanical Engineering, 2015, Georgia Tech

Regime recognition is an important tool used in creation of usage spectra and fatigue loads analysis. While a variety of regime recognition algorithms have been developed and deployed to date, verification and validation (V&V) of such algorithms is still a labor intensive process that is largely subjective. The current V&V process for regime recognition codes involves a comparison of scripted flight test data to regime recognition algorithm outputs. This is problematic because scripted flight test data is expensive to obtain, may not accurately match the maneuver script, and is often used to train the regime recognition algorithms and thus is not appropriate for V&V purposes. In this paper, a simulation-based virtual pilot algorithm is proposed as an alternative to physical testing for generating V&V flight test data. A “virtual pilot” is an algorithm that replicates a human’s piloting and guidance role in simulation by translating high level maneuver instructions into parameterized control laws. Each maneuver regime is associated with a feedback control law, and a control architecture is defined which provides for seamless transitions between maneuvers and allows for execution of an arbitrary maneuver script in simulation. The proposed algorithm does not require training data, iterative learning, or optimization, but rather utilizes a tuned model and feedback control laws defined for each maneuver. As a result, synthetic HUMS data may be generated and used in a highly automated regime recognition V&V process. In this thesis, the virtual pilot algorithm is formulated and the component feedback control laws and maneuver transition schemes are defined. Example synthetic HUMS data is generated using a simulation model of the SH-60B, and virtual pilot fidelity is demonstrated through both conformance to the ADS-33 standards for selected Mission Task Elements and comparison to actual HUMS data. Advisors/Committee Members: Rogers, Jonathan D. (advisor), Ferri, Aldo A. (committee member), Singhose, William E. (committee member).

Subjects/Keywords: Regime recognition; Verification and validation; V&V; Virtual pilot; Helicopter flight control; Maneuver control

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

APA (6th Edition):

Fowler, L. E. (2015). A Virtual pilot algorithm for synthetic HUMS data generation. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54473

Chicago Manual of Style (16th Edition):

Fowler, Lee Everett. “A Virtual pilot algorithm for synthetic HUMS data generation.” 2015. Masters Thesis, Georgia Tech. Accessed October 16, 2019. http://hdl.handle.net/1853/54473.

MLA Handbook (7th Edition):

Fowler, Lee Everett. “A Virtual pilot algorithm for synthetic HUMS data generation.” 2015. Web. 16 Oct 2019.

Vancouver:

Fowler LE. A Virtual pilot algorithm for synthetic HUMS data generation. [Internet] [Masters thesis]. Georgia Tech; 2015. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1853/54473.

Council of Science Editors:

Fowler LE. A Virtual pilot algorithm for synthetic HUMS data generation. [Masters Thesis]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54473

2. Herzig, Sebastian J. I. A Bayesian learning approach to inconsistency identification in model-based systems engineering.

Degree: PhD, Mechanical Engineering, 2015, Georgia Tech

Designing and developing complex engineering systems is a collaborative effort. In Model-Based Systems Engineering (MBSE), this collaboration is supported through the use of formal, computer-interpretable models, allowing stakeholders to address concerns using well-defined modeling languages. However, because concerns cannot be separated completely, implicit relationships and dependencies among the various models describing a system are unavoidable. Given that models are typically co-evolved and only weakly integrated, inconsistencies in the agglomeration of the information and knowledge encoded in the various models are frequently observed. The challenge is to identify such inconsistencies in an automated fashion. In this research, a probabilistic (Bayesian) approach to abductive reasoning about the existence of specific types of inconsistencies and, in the process, semantic overlaps (relationships and dependencies) in sets of heterogeneous models is presented. A prior belief about the manifestation of a particular type of inconsistency is updated with evidence, which is collected by extracting specific features from the models by means of pattern matching. Inference results are then utilized to improve future predictions by means of automated learning. The effectiveness and efficiency of the approach is evaluated through a theoretical complexity analysis of the underlying algorithms, and through application to a case study. Insights gained from the experiments conducted, as well as the results from a comparison to the state-of-the-art have demonstrated that the proposed method is a significant improvement over the status quo of inconsistency identification in MBSE. Advisors/Committee Members: Paredis, Christiaan J. J. (advisor), McGinnis, Leon F. (committee member), Rogers, Jonathan D. (committee member), Basole, Rahul C. (committee member), Ender, Tommer R. (committee member).

Subjects/Keywords: Interoperability; Bayesian learning; Bayesian reasoning; Graph queries; Pattern matching; Abductive reasoning; Incremental reasoning; Automated reasoning; Machine learning; RDF; SPARQL; Semantic web; MBSE; Model-based systems engineering; Inconsistency management; Inconsistency identification; Model integration

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

APA (6th Edition):

Herzig, S. J. I. (2015). A Bayesian learning approach to inconsistency identification in model-based systems engineering. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53576

Chicago Manual of Style (16th Edition):

Herzig, Sebastian J I. “A Bayesian learning approach to inconsistency identification in model-based systems engineering.” 2015. Doctoral Dissertation, Georgia Tech. Accessed October 16, 2019. http://hdl.handle.net/1853/53576.

MLA Handbook (7th Edition):

Herzig, Sebastian J I. “A Bayesian learning approach to inconsistency identification in model-based systems engineering.” 2015. Web. 16 Oct 2019.

Vancouver:

Herzig SJI. A Bayesian learning approach to inconsistency identification in model-based systems engineering. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1853/53576.

Council of Science Editors:

Herzig SJI. A Bayesian learning approach to inconsistency identification in model-based systems engineering. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53576

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