Deep Learning and NLP-Based Trend Analysis in Actuators and Power Electronics

Actuators and power electronics are fundamental components of modern control systems, enabling high-precision functionality, enhanced energy efficiency, and sophisticated automation. This study investigates evolving research trends and thematic developments in these areas spanning the last two decad...

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Vydané v:Actuators Ročník 14; číslo 8; s. 379
Hlavní autori: Jung, Woojun, Cho, Keuntae
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.08.2025
Predmet:
Actuators
Adaptive control
Analysis
Artificial intelligence
Automation
BERTopic
Cluster analysis
Clustering
Control systems
Electronics
Energy efficiency
Energy industry
Energy management systems
Forecasts and trends
Hydraulic equipment
Innovations
intelligent control systems
Online databases
Piezoelectric actuators
Power electronics
research trend analysis
Semiconductors
Trend analysis
Trends
ISSN:2076-0825, 2076-0825
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Abstract Actuators and power electronics are fundamental components of modern control systems, enabling high-precision functionality, enhanced energy efficiency, and sophisticated automation. This study investigates evolving research trends and thematic developments in these areas spanning the last two decades (2005–2024). This study analyzed 1840 peer-reviewed abstracts obtained from the Web of Science database using BERTopic modeling, which integrates transformer-based sentence embeddings with UMAP for dimensionality reduction and HDBSCAN for clustering. The approach also employed class-based TF-IDF calculations, intertopic distance visualization, and hierarchical clustering to clarify topic structures. The analysis revealed a steady increase in research publications, with a marked surge post-2015. From 2005 to 2014, investigations were mainly focused on established areas including piezoelectric actuators, adaptive control, and hydraulic systems. In contrast, the 2015–2024 period saw broader diversification into new topics such as advanced materials, robotic mechanisms, resilient systems, and networked actuator control through communication protocols. The structural topic analysis indicated a shift from a unified to a more differentiated and specialized spectrum of research themes. This study offers a rigorous, data-driven outlook on the increasing complexity and diversity of actuator and power electronics research. The findings are pertinent for researchers, engineers, and policymakers aiming to advance state-of-the-art, sustainable industrial technologies.
AbstractList Actuators and power electronics are fundamental components of modern control systems, enabling high-precision functionality, enhanced energy efficiency, and sophisticated automation. This study investigates evolving research trends and thematic developments in these areas spanning the last two decades (2005–2024). This study analyzed 1840 peer-reviewed abstracts obtained from the Web of Science database using BERTopic modeling, which integrates transformer-based sentence embeddings with UMAP for dimensionality reduction and HDBSCAN for clustering. The approach also employed class-based TF-IDF calculations, intertopic distance visualization, and hierarchical clustering to clarify topic structures. The analysis revealed a steady increase in research publications, with a marked surge post-2015. From 2005 to 2014, investigations were mainly focused on established areas including piezoelectric actuators, adaptive control, and hydraulic systems. In contrast, the 2015–2024 period saw broader diversification into new topics such as advanced materials, robotic mechanisms, resilient systems, and networked actuator control through communication protocols. The structural topic analysis indicated a shift from a unified to a more differentiated and specialized spectrum of research themes. This study offers a rigorous, data-driven outlook on the increasing complexity and diversity of actuator and power electronics research. The findings are pertinent for researchers, engineers, and policymakers aiming to advance state-of-the-art, sustainable industrial technologies.
Audience Academic
Author Jung, Woojun
Cho, Keuntae
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SubjectTerms Actuators
Adaptive control
Analysis
Artificial intelligence
Automation
BERTopic
Cluster analysis
Clustering
Control systems
Electronics
Energy efficiency
Energy industry
Energy management systems
Forecasts and trends
Hydraulic equipment
Innovations
intelligent control systems
Online databases
Piezoelectric actuators
Power electronics
research trend analysis
Semiconductors
Trend analysis
Trends
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Title Deep Learning and NLP-Based Trend Analysis in Actuators and Power Electronics
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Volume 14
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