Antennas and Propagation Research From Large-Scale Unstructured Data With Machine Learning: A review and predictions
Antennas and Propagation Research From Large-Scale Unstructured Data With Machine Learning: A review and predictions
复制标题
DOI：
10.1109/map.2023.3290385
复制
发表时间：
2023-10
影响因子：
3.5
通讯作者：
Young-ok Cha;A. Ihalage;Yang Hao
中科院分区：
文献类型：
--
作者：
Young-ok Cha;A. Ihalage;Yang Hao
在过去的世纪里，天线与传播（A&P）的研究取得了显著的进展，它给我们的社会和生活带来了巨大的变化，并导致了工程和技术的范式转变。虽然电磁学的基础理论已经建立并成熟，但A&P的研究将继续在第四次工业革命中发挥重要作用。在本文中，我们提出了一种基于自然语言处理（NLP）和机器学习（ML）技术的方法，以审查基于公开发表的科学论文和专利的大规模非结构化数据的A&P研究，并提供有意义的总结和预测信息。我们特别筛选了1981年至2021年间发表的159，000篇研究论文，并提取了2，415个反映A& P过去和现在关键研究主题的重要关键词，然后应用具有集成注意力机制的编码器-解码器长短期记忆（LSTM）网络，以Gartner炒作周期的形式预测A&P研究的未来趋势。
The past century has witnessed remarkable progress in antennas and propagation (A&P) research, which has made dramatic changes to our society and life and has led to paradigm shifts in engineering and technology. Although the underlying theory of electromagnetics is well established and mature, research on A&P will continue to play a paramount role in the Fourth Industrial Revolution. In this article, we present an approach based on natural language processing (NLP) and machine learning (ML) techniques to review A&P research based on large-scale unstructured data from openly published scientific papers and patents and, in turn, provide meaningful summative and predictive information. We particularly screen 159,000 research papers published between 1981 and 2021 and extract a pool of 2,415 significant keywords reflecting past and present key research topics in A&P. We then apply an encoder–decoder long short-term memory (LSTM) network with an integrated attention mechanism to predict the future trends of A&P research in the form of a Gartner’s hype cycle.