[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128387-en":3,"doc-seo-128387-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128387,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Indoor Safety of Wireless Power Transfer - A Machine Learning Approach","This paper presents a machine learning–driven approach for far-field wireless power transfer (WPT) in indoor settings. A radar detects the presence and height of individuals, while node locations and the propagation channel feed an ML model that predicts the optimal transmit power per transmitter. The goal is to form a safe three-dimensional zone around people while improving power transfer efficiency. Random forest, support vector machine, and neural network models are evaluated; neural networks achieve superior performance, keeping electric field amplitude below thresholds across multiple scenarios and outperforming maximum ratio transmission in many measurements.","Indoor Safety of Wireless Power Transfer: A Machine  \nLearning Approach  \nPouya Mehrjouseresht, Student Member, IEEE, Vladimir Volski, Member, IEEE, Ihsane Gryech, Robbert Beerten, Student Member, IEEE, Alexander Ye. Svezhentsev, Senior Member, IEEE, Marco Mercuri, Senior Member, IEEE, Ping Jack Soh, Senior Member, IEEE, Dominique M. M.-P. Schreurs, Fellow, IEEE,  \nAbstract—This paper introduces an innovative approach integrating Machine Learning (ML) methods into a far-field Wireless Power Transfer (WPT) system. Within this system, a radar is employed to detect the presence and height of individuals. These data, combined with information on the location of nodes, as well as the propagation channel between transmitters and nodes, are fed into the proposed ML algorithm. The ML model aims to predict the optimal power level for each transmitter, effectively establishing a safe three-dimensional zone around people while also maximizing power transfer efficiency. The advantages of using ML are the realization of a real-time system, which is crucial in indoor applications, keeping dangerous radiation at a safe level with a very low risk of harmful exposure, and simultaneously enhancing efficiency. Three ML models are evaluated, namely random forest (RF), support vector machine (SVM), and neural network (NN). Simulation results highlight the superior performance of the NN model, demonstrating its ability to effectively capture the complex nonlinear characteristics of indoor propagation environments, with only approximately 6% of its predictions exceeding the predefined safety threshold. The experimental results show that NN-based WPT can maintain the electric field amplitude (EFA) below a defined threshold for multiple indoor experimental scenarios over the person’s height. In addition, the proposed approach outperformed the maximum ratio transmission (MRT) approach in terms of radio frequencyradio frequency (RF-RF) transmission efficiency in 21.43% of the measurements conducted with multiple people present in the testbed.  \nIndex Terms—Electric field amplitude, machine learning, neural networks, power transfer efficiency, radar, radio frequency, random forest, support vector machine, safe electromagnetic radiation, wireless power transfer  \nI. Introduction  \nFar-field wireless power transfer (WPT) is an emerging  \ntechnology [1]–[5] that faces two important challenges:  \nManuscript received April 19, 2021; revised August 16, 2021 . This research was funded by the Flemish FWO project (Collaborative Smart Surfaces in Home Materials for Internet of Things Wireless Powering, grant number: G0B9821N).(Corresponding author: Pouya Mehrjouseresht)  \nPouya Mehrjouseresht, Vladimir Volski, Ihsane Gryech, Robbert Beerten, and Dominique M. M.-P. Schreurs are with Waves: Core Research and Engineering (WaveCoRE), Department of Electrical Engineering (ESAT), KU Leuven, B-3001 Leuven, Belgium (e-mail: [pouya.mehrjouseresht@kuleuven.be](pouya.mehrjouseresht@kuleuven.be))  \nAlexander Ye. Svezhentsev is with O. Y. Usikov Institute of Radiophysics and Electronics, National Academy of Sciences of Ukraine, Kharkiv, 61085, Ukraine.  \nMarco Mercuri is with Dipartimento Ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile (DIIES), University Mediterranea of Reggio Calabria, 89124 Reggio Calabria, Italy.  \nPing Jack Soh is with Centre for Wireless Communication, University of Oulu, 90570 Oulu, Finland  \nhazardous electromagnetic radiation (EMR) [6], [7] and low efficiency [8]–[10] . Excessive exposure to EMR can cause adverse biological effects, such as tissue heating and metabolic disruption at high frequencies, making uncontrolled WPT operation a potential health risk. Because of this, the International Commission on Non-Ionizing Radiation Protection (ICNIRP) has established exposure guidelines. The reference level of electric field strength in the 2-300 GHz band is approximately 60 V/m [6] . Importantly, higher transmit power gener","cbCaiexTKUCQDYFG","https://ap.wps.com/l/cbCaiexTKUCQDYFG","pdf",4206850,4,1,13,"English","en",105,"# Abstract\n# Index Terms\n# I. 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