[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118874-en":3,"doc-seo-118874-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118874,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication - An Overview - Research summary","This study evaluates machine learning-based approaches for improving the performance and reliability of Power Line Communication (PLC) systems. PLC technology plays a key role in energy management, monitoring, and automation, where stable and efficient communication is essential. A comprehensive review of prior research and practical applications guides the investigation, focusing on how different machine learning methods can enhance PLC efficacy and stability. Experiments and simulations assess techniques including deep learning, support vector machines, and random forests, with results indicating meaningful operational improvements and supporting the proposed hypothesis.","UC Riverside  \nUC Riverside Previously Published Works  \nTitle  \nMachine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview  \nPermalink  \n[https://escholarship.org/uc/item/0xv3t2xd](https://escholarship.org/uc/item/0xv3t2xd)  \nAuthors  \nAkinci, Tahir Cetin  \nErdemir, Gokhan  \nZengin, A Tariket al.  \nPublication Date  \n2023  \nDOI  \n10.1109/access.2023.3330690  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nReceived 8 October 2023, accepted 3 November 2023, date of publication 6 November 2023, date of current version 10 November 2023. Digital Object Identifier 10.1109/ACCESS.2023.3330690  \nMachine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication: An Overview  \nTAHIR CETIN AKINCI1,2,(Senior Member, IEEE),  \nGOKHAN ERDEMIR3,(Senior Member, IEEE), A. TARIK ZENGIN2,  \nSERHAT SEKER2, AND ABDOULKADER IBRAHIM IDRISS4  \n1WCGEC, University of California at Riverside (UCR), Riverside, CA 92521, USA  \n2Electrical Engineering Department, Istanbul Technical University (ITU), 344690 İstanbul, Turkey  \n3Engineering Management and Technology, The University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA  \n4Department of Electrical and Energy Engineering, Faculty of Engineering, Université de Djibouti, Djibouti City, Djibouti Corresponding author: Tahir Cetin Akinci ([tahircetin.akinci@ucr.edu](tahircetin.akinci@ucr.edu))  \nABSTRACT This study endeavors to investigate the effectiveness of machine learning-based methodologies in enhancing the performance and reliability of Power Line Communication (PLC) systems. PLC systems constitute a critical component within the domains of energy management, monitoring, and automation. The fundamental objective herein is to contribute significantly to the scholarly discourse by conducting a comprehensive review encompassing research investigations and practical applications documented in the extant literature. The primary motivation underpinning this research is predicated upon the necessity for a meticulous evaluation of machine learning techniques that hold the potential to enhance the efficacy and stability of PLC systems. The deployment of these techniques bears the promise of engendering heightened levels of efficiency across the spectrum of energy management, communication, and automation systems. Within this scholarly quest, the study posits a hypothesis: Machine learning-based methodologies possess the capacity to effect marked improvements in the performance and reliability of PLC systems. Methodological scrutiny is executed through a comprehensive evaluation of diverse machine learning techniques, including, but not limited to, deep learning, support vector machines, and random forests, facilitated by a series of empirical experiments and simulations. Empirical findings resoundingly corroborate the proposition, substantiating a significant enhancement in the operational performance of PLC systems when these machine learning methods are judiciously employed. In summation, this study assumes the role of a catalyst in exploring latent, untapped potential inherent within machine learning-based methodologies, customarily calibrated to resonate within the intricate matrix of PLC systems. The zenith of this rigorous investigation stands poised to illuminate the path toward transformative advancements in the domains of energy management, communication, monitoring, and automation systems. The findings contribute significantly to the academic discourse, offering a compass for future research inquiries and practical applications within this burgeoning and dynamic field. ","cbCaio1w2yMKvl8D","https://ap.wps.com/l/cbCaio1w2yMKvl8D","pdf",1620013,1,23,"English","en",105,"# Introduction\n## Power Line Communication (PLC) overview and applications\n## Broadband PLC and protocol support\n## PLC as a data transmission medium","[{\"question\":\"What is the main focus of the study on power line communication (PLC)?\",\"answer\":\"The study investigates how machine learning-based methods can enhance the performance and reliability of PLC systems for energy management, monitoring, and automation.\"},{\"question\":\"Which machine learning techniques are evaluated for PLC improvements?\",\"answer\":\"The work evaluates multiple approaches, including deep learning, support vector machines, and random forests, using experiments and simulations.\"},{\"question\":\"What do the results indicate about using machine learning in PLC systems?\",\"answer\":\"The findings support the hypothesis that applying machine learning methods can produce significant improvements in the operational performance of PLC systems when used appropriately.\"}]","Machine Learning-Based Error Correction Codes and Communication Protocols for Power Line Communication - 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