{"583489":{"#nid":"583489","#data":{"type":"event","title":"PhD Proposal by Matthew Gross","body":[{"value":"\u003Cdiv\u003E\r\n\u003Cp\u003EPh.D. Thesis Proposal by\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EMatthew Gross\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAdvisor: \u003C\/strong\u003EDr. Mark Costello\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMETA-OPTIMIZATION WITH APPLICATION TO AEROSPACE SYSTEM IDENTIFICATION\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003E3 PM Tuesday, November 15, 2016\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMontgomery-Knight Room 317\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Optimization is a powerful tool for solving practical engineering problems, including identification for aerospace systems.\u0026nbsp; Due to the complexity of many optimization problems, numerical methods are often employed to obtain solutions.\u0026nbsp; A diverse collection of numerical optimization methods have been developed, each suited for different types of problems.\u0026nbsp; Local search methods are highly efficient on convex problems, but require good initial guesses to find the global optimum.\u0026nbsp; Global methods are able to search the entire parameter space to determine the global optimum, often at the cost of increased computation time.\u0026nbsp; Given the fact that no one optimization algorithm performs well on every problem, the selection of the proper algorithm for a given problem is a critical decision by an engineer.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; To overcome the algorithm selection problem, this work proposes a new method for automatically selecting and deploying optimizers, dubbed meta-optimization.\u0026nbsp; The goal of meta-optimization is to intelligently deploy a diverse set of optimization algorithms, leveraging the strengths of each algorithm and minimizing their weaknesses in order to reliably and accurately solve challenging optimization problems with minimal user intervention.\u0026nbsp; The first component of the meta-optimizer is the bank of optimization algorithms which includes numerous local and global search methods.\u0026nbsp; Algorithm selection is performed in an online manner, choosing the most effective algorithms at the current stage of the solution process.\u0026nbsp; The meta-optimizer must also ensure smooth transition between different algorithms and prevent premature convergence in local optima.\u0026nbsp; Finally, the meta-optimizer tunes optimization algorithms parameters which degrade performance of the algorithm.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; The meta-optimizer is first tested on a set of benchmark functions and then used to solve two aerospace system identification problems.\u0026nbsp; The first problem considered is the estimation of parameters for a new smart projectile system.\u0026nbsp; Both simulated and experimental spark range data is used to estimate the body and control mechanism parameters.\u0026nbsp; The second application is the calibration of inertial measurement units (IMUs) for onboard sensing of precision guided air drop systems using simulated and experimental calibration table data.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee Members\u003C\/strong\u003E\u003C\/p\u003E\r\n\u003C\/div\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Mark Costello, AE (Advisor)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Brian German, AE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Eric Johnson, AE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Graeme Kennedy, AE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Aldo Ferri, ME\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Meta-Optomization with Application to Aerospace System Identification"}],"uid":"27707","created_gmt":"2016-11-03 16:28:09","changed_gmt":"2016-11-03 16:28:29","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2016-11-15T15:00:00-05:00","event_time_end":"2016-11-15T17:00:00-05:00","event_time_end_last":"2016-11-15T17:00:00-05:00","gmt_time_start":"2016-11-15 20:00:00","gmt_time_end":"2016-11-15 22:00:00","gmt_time_end_last":"2016-11-15 22:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}