[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126268-en":3,"doc-seo-126268-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126268,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Machine learning based Prediction and Optimization of Iron Loss - Bachelor’s thesis 2025","A machine learning method is developed to predict high-frequency iron core losses with improved accuracy over limited high-frequency sinusoidal excitation models. A magnetic-materials database is built across different frequencies and temperatures, using key features such as excitation waveform, magnetic flux density, frequency, and temperature. Excitation waveform classification and iron-loss prediction are performed via an integrated strategy using XGBoost. Experimental results show higher accuracy, lower MSE, and R² closer to one. Genetic algorithm optimization further jointly minimizes iron loss and maximizes magnetic energy transfer for better energy efficiency. The study supports optimal magnetic component design for high power density power electronics and suggests future deep learning and transfer learning.","Machine learning based Prediction and Optimization of Iron Loss  \nLappeenranta-Lahti University of Technology LUT  \nBachelor’s Programme in Electrical Engineering, Bachelor's thesis 2025  \nYifan Ai  \nExaminer(s): Associate Professor Jianliang Chen  \nD. Sc Mina Valikhany  \nABSTRACT  \nLappeenranta–Lahti University of Technology LUTLUT School of Energy Systems  \nElectrical Engineering  \nIn co-operation with partner university: Hebei University of Technology  \nYifan Ai  \nMachine learning based Prediction and Optimization of Iron Loss  \nBachelor’s thesis  \n2025  \n49 pages, 12 figures, 9 tables and 0 appendices  \nExaminer(s): Assoc. Prof. Jianliang Chen and D. Sc. Mina Valikhany  \nKey words: Magnetic core loss; Machine learning; XGBoost; Excitation waveform classification; Genetic algorithm.  \nIn this thesis, a machine learning-based method for predicting high-frequency iron core losses is proposed. To address the problem of limited accuracy of high-frequency sinusoidal excitation, a database of magnetic materials, different frequencies and temperatures is established. The key features extracted include excitation waveform, magnetic flux density, frequency and temperature. An integrated learning strategy is used to classify the excitation waveform and predict the iron loss by XGBoost. By analyzing the experimental results, it is concluded that the method has higher prediction accuracy, lower mean square error (MSE), and a coefficient of determination (R²) closer to one. Meanwhile, in order to achieve the dual-objective optimization of minimizing iron loss and maximizing magnetic energy transfer, this study also optimizes the magnetic element design using genetic algorithm (GA) . The energy efficiency ratio of the magnetic element can be further improved by this method. Experimental results verify the effectiveness of the method under high-frequency conditions and provide reliable support for the optimal design of magnetic components for high power density power electronic systems. Future research can combine deep learning and migration learning to improve the adaptive capability of the model and extend it to a wider range of magnetic materials and application scenarios.  \nACKNOWLEDGEMENTS  \nIn the process of completing this thesis, I have gained support and encouragement from all walks of life.  \nFirst of all, I would like to express my deepest gratitude to my two supervisors. Assoc. Prof. Jianliang Chen from Hebei University of Technology and Dr. Mina Valikhany from Lappeenranta University of Technology. Prof. Chen has guided me through every detail with his wisdom and patience. You made me realize that rigor and curiosity are the two pillars of academic research.  \nAlso, I'd like to express my particular gratitude to Prof. Changgeng Zhang for his values support during the past 3 years. Your support have been like a light leading me through the foggy expanse of scientific research.  \nMost of thanks to Assoc. Prof. Lasse Laurila and Assoc. Prof. Mohammad Khan from LUT. Your insights have been an indispensable vein in this study through lively discussions at each monthly seminar.  \nTo my family, no words can hold my gratitude. Your love and support is the compass that guides me through snowfields.  \nTo my fellow students and dear friends, who have traveled thousands of miles from China to Finland. Thank you for your tolerance and support. Your resilience and kindness have taught me that growth is always nurtured in community.  \nTo basketball, heartbeat of my Finnish winter rhythms. For those of friends who have become family under the light at sport hall, you made me realize that happiness is an important role in academics.  \nAll the kindness you give to me will stay in my mind. Like the midnight sun in Finnish midsummer, your warmth will never fade.  \n最后，留下小艾同学对未来自己的祝福：道阻且长，祝好！  \n4  \nTable of contents  \n1 Introduction ....................................................................................................................","cbCaikwBtYA5iqb3","https://ap.wps.com/l/cbCaikwBtYA5iqb3","pdf",2618973,5,1,49,"English","en",105,"# Introduction\n## Research background and application scenarios\n## Design challenges of high-frequency magnetic components\n## Iron loss problem\n## Thermal management issues\n## Selection of magnetic materials\n## Role of machine learning in the design of magnetic elements\n## Advantages of machine learning in iron loss modeling\n## Application of machine learning in magnetic element optimization\n## Study objectives and major contributions\n## Study Objectives\n## Main contributions\n# Iron loss mechanism of high-frequency magnetic components\n## Classification of magnetic core loss\n## Hysteresis loss\n## Eddy current loss\n## Excess magnetic loss\n## Impact factors of iron loss in the high-frequency environment","[{\"question\":\"What problem does the thesis address in iron-loss prediction?\",\"answer\":\"It targets the limited accuracy of high-frequency sinusoidal excitation models for predicting high-frequency iron core losses.\"},{\"question\":\"What data and features are used to build the machine learning approach?\",\"answer\":\"It establishes a database over magnetic materials, frequencies, and temperatures, extracting features including excitation waveform, magnetic flux density, frequency, and temperature.\"},{\"question\":\"How are both prediction accuracy and magnetic element design optimized?\",\"answer\":\"XGBoost is used with an integrated learning strategy to classify excitation waveform and predict iron loss, while a genetic algorithm optimizes the magnetic element to minimize iron loss and maximize magnetic energy transfer.\"}]","Machine learning based Prediction and Optimization of Iron Loss - Bachelor’s thesis 2025 | PDF",1785904157,123,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-based-prediction-and-optimization-of-iron-loss-bachelors-thesis-2025","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-based-prediction-and-optimization-of-iron-loss-bachelors-thesis-2025/126268/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does the thesis address in iron-loss prediction?","Question",{"text":77,"@type":78},"It targets the limited accuracy of high-frequency sinusoidal excitation models for predicting high-frequency iron core losses.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What data and features are used to build the machine learning approach?",{"text":82,"@type":78},"It establishes a database over magnetic materials, frequencies, and temperatures, extracting features including excitation waveform, magnetic flux density, frequency, and temperature.",{"name":84,"@type":75,"acceptedAnswer":85},"How are both prediction accuracy and magnetic element design optimized?",{"text":86,"@type":78},"XGBoost is used with an integrated learning strategy to classify excitation waveform and predict iron loss, while a genetic algorithm optimizes the magnetic element to minimize iron loss and maximize magnetic energy transfer.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]