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- W4311486385 abstract "Abstract To intelligently identify emerging technology research directions at an early stage, this study proposes a method for the early identification of emerging technology directions on the basis of weak signals and explores the effectiveness of the method. With signal combination map theory, the weak signal keywords of emerging technologies are extracted by text mining. An unsupervised learning model (LDA) is used for topic clustering to further improve the signal interpretation ability and identify weak signal topics. A deep learning model (BERT) is utilized to extend the weak signal topics and make the topic directions clear. By applying the method to augmented reality technology and basing on the data of invention patents and scientific studies in this field in the last decade, we identify six research topics with weak signal characteristics in current augmented reality research; these six are architecture, industrial manufacturing and maintenance, medical care, education, and augmented reality contact lenses, which may be the development directions of future augmented reality research. The weak signal topics are then further expanded to clearly show the specific content of these topics in terms of function, domain, and technology while proving that the early identification method is effective and feasible." @default.
- W4311486385 created "2022-12-26" @default.
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- W4311486385 date "2022-12-14" @default.
- W4311486385 modified "2023-10-15" @default.
- W4311486385 title "Early Identification Methods for Emerging Technologies Based on Weak Signals" @default.
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- W4311486385 doi "https://doi.org/10.21203/rs.3.rs-2291140/v1" @default.
- W4311486385 hasPublicationYear "2022" @default.
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