{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T06:05:11Z","timestamp":1781849111441,"version":"3.54.5"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Institute of Civil Military Technology Cooperation funded by the Defense Acquisition Program Administration and Ministry of Trade, Industry and Energy of Korean government","doi-asserted-by":"publisher","award":["21-SF-EC-02"],"award-info":[{"award-number":["21-SF-EC-02"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Improvement of wireless power transfer (WPT) systems is necessary to tackle issues of power transfer efficiency, high costs due to sensor and communication requirements between the transmitter (Tx) and receiver (Rx), and maintenance problems. Analytical techniques and hardware-based synchronization research for Rx-sensorless WPT may not always have been available or accurate. To address these limitations, researchers have recently employed machine learning (ML) to improve efficiency and accuracy. The objective of this work was to replace Tx\u2013Rx communication with ML, utilizing Tx-side parameters to predict the load and coupling coefficients on an LC\u2013LC tuned WPT system. Based on current and voltage features collected on the Tx-side for various load and coupling coefficient values, we developed two models for each load and coupling prediction. This study demonstrated that the extra trees regressor effectively predicted the characteristics of LC\u2013LC tuned WPT systems, with coefficients of determination of 0.967 and 0.996 for load and coupling, respectively. Additionally, the mean absolute percentage errors were 0.11% and 0.017%.<\/jats:p>","DOI":"10.3390\/s24020501","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T07:25:07Z","timestamp":1705303507000},"page":"501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Predicting Receiver Characteristics without Sensors in an LC\u2013LC Tuned Wireless Power Transfer System Using Machine Learning"],"prefix":"10.3390","volume":"24","author":[{"given":"Minhyuk","family":"Kim","sequence":"first","affiliation":[{"name":"EM Environment R&D Department, Korea Automotive Technology Institute, Cheonan 31214, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wend Yam Ella Flore","family":"Niada","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Daegu University, Gyeongsan 38453, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangwook","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Soonchunhyang University, Asan 31538, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fanpeng, K., Yi, H., and Najafizadeh, L. 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