{"634236":{"#nid":"634236","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Soyeon  Jeong","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003EApplications Of Machine Learning Strategy For Wireless Power Transfer And Identification\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Manos Tentzeris, ECE, Chair , Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Andrew Peterson, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Gregory Durgin, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Sangkil Kim, Pusan National Univ\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Yang Wang, CEE\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe objective of the presented\u0026nbsp;research is to propose and demonstrate ML applications of wireless power transfer and identification technology. Several works describe the implementation of a Machine Learning (ML) strategy based on 1) the use of Neural Networks (NN) for real-time range adaptive automatic impedance matching of Wireless Power Transfer (WPT) applications, 2) the Naive Bayes algorithm for the prediction of the drone\u0026rsquo;s position, thus enhancing the WPT efficiency, and 3) the Support Vector Machine (SVM) classification strategy for read\/interrogation enhancement in chipless RFID applications. The ML approach for the effective prediction of the optimal parameters of the tunable matching network, and classification range-adaptive transmitter coils (Tx) is introduced, aiming to achieve an effective automatic impedance matching over a wide range of relative distances. A novel WPT system consisting of a tunable matching circuit and 3 Tx coils which have different radius controlled by trained NN models is characterized. A proof-of-concept WPT platform which allows the accurate prediction of the drone\u0026rsquo;s position based on the flight data utilizing ML classification using the Naive Bayes algorithm is also given. A MLbased approach for classification and of detection tag IDs has been presented, which can perform effective transponder readings for a wide variety of ranges and contexts, while providing high tag-ID detection accuracy. A SVM algorithm was trained using measurement data, and its accuracy was tested and characterized as a function of the included training data. In summary, this research sets a precedent, opening the door to a rich and wide area of research for the implementation of ML methods for the enhancement of WPT and chipless RFID applications.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Applications Of Machine Learning Strategy For Wireless Power Transfer And Identification "}],"uid":"28475","created_gmt":"2020-04-09 18:27:23","changed_gmt":"2020-04-09 18:45:02","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2020-04-17T16:00:00-04:00","event_time_end":"2020-04-17T18:00:00-04:00","event_time_end_last":"2020-04-17T18:00:00-04:00","gmt_time_start":"2020-04-17 20:00:00","gmt_time_end":"2020-04-17 22:00:00","gmt_time_end_last":"2020-04-17 22:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"},{"id":"1808","name":"graduate students"}],"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":""}}}