Research and development in hypersonics have progressed significantly in
recent years, with various military and commercial applications being
demonstrated increasingly. Public and private organizations in several
countries have been investing in hypersonics, with the aim to overtake their
competitors and secure/improve strategic advantage and deterrence. For these
organizations, being able to identify emerging technologies in a timely and
reliable manner is paramount. Recent advances in information technology have
made it possible to analyze large amounts of data, extract hidden patterns, and
provide decision-makers with new insights. In this study, we focus on
scientific publications about hypersonics within the period of 2000-2020, and
employ natural language processing and machine learning to characterize the
research landscape by identifying 12 key latent research themes and analyzing
their temporal evolution. Our publication similarity analysis revealed patterns
that are indicative of cycles during two decades of research. The study offers
a comprehensive analysis of the research field and the fact that the research
themes are algorithmically extracted removes subjectivity from the exercise and
enables consistent comparisons between topics and between time intervals.

该研究利用自然语言处理和机器学习技术，分析 2000-2020 年期间与高超声速相关的学术出版物，从中提取出 12 个关键的潜在研究主题，并分析它们随时间的演变。研究发现，出版物之间的相似性反映了两个研究十年周期的模式。该研究为该领域的研究提供了全面的分析。

高超声速研究的演化：自然语言处理与机器学习的应用

On the evolution of research in hypersonics: application of natural language processing and machine learning

Emerging technologies can have major economic impacts and affect strategic
stability. Yet, early identification of emerging technologies remains
challenging. In order to identify emerging technologies in a timely and
reliable manner, a comprehensive examination of relevant scientific and
technological (S&T) trends and their related references is required. This
examination is generally done by domain experts and requires significant
amounts of time and effort to gain insights. The use of domain experts to
identify emerging technologies from S&T trends may limit the capacity to
analyse large volumes of information and introduce subjectivity in the
assessments. Decision support systems are required to provide accurate and
reliable evidence-based indicators through constant and continuous monitoring
of the environment and help identify signals of emerging technologies that
could alter security and economic prosperity. For example, the research field
of hypersonics has recently witnessed several advancements having profound
technological, commercial, and national security implications. In this work, we
present a multi-layer quantitative approach able to identify future signs from
scientific publications on hypersonics by leveraging deep learning and weak
signal analysis. The proposed framework can help strategic planners and domain
experts better identify and monitor emerging technology trends.

提出了一种基于深度学习和弱信号分析的多层定量方法，能够从高超声速科学出版物中识别未来的迹象，帮助战略规划者和领域专家更好地识别和监测新兴技术趋势，以提供准确可靠、基于证据的指标。