A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)

Systems, Journal Year: 2023, Volume and Issue: 11(8), P. 400 - 400

Published: Aug. 2, 2023

The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers substantial investments this expansive field. However, due to wide range topics subdomains within UAV research, newcomers may find themselves overwhelmed by numerous options available. It is therefore crucial for those involved research recognize its interdisciplinary nature connections with other disciplines. This paper presents comprehensive overview field, highlighting recent trends advancements. Drawing on literature reviews surveys, review begins classifying UAVs based their flight characteristics. then provides an current UAVs, utilizing data Scopus database quantify number documents associated each direction interconnections. also explores potential areas further development including communication, artificial intelligence, remote sensing, miniaturization, swarming cooperative control, transformability. Additionally, it discusses aircraft commonly used control techniques, appropriate algorithms research. Furthermore, addresses general hardware software architecture applications, key issues them. open source projects By presenting view aims enhance our understanding rapidly evolving highly area

Language: Английский

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Deep transfer learning for automatic speech recognition: Towards better generalization

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 277, P. 110851 - 110851

Published: July 29, 2023

Language: Английский

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A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 3768 - 3789

Published: Jan. 1, 2024

Automating the monitoring of industrial processes has potential to enhance efficiency and optimize quality by promptly detecting abnormal events thus facilitating timely interventions. Deep learning, with its capacity discern non-trivial patterns within large datasets, plays a pivotal role in this process. Standard deep learning methods are suitable solve specific task given type data. During training, demands volumes labeled However, due dynamic nature environment, it is impractical acquire large-scale data for standard training every slightly different case anew. transfer offers solution problem. By leveraging knowledge from related tasks accounting variations distributions, framework solves new little or even no additional The approach bypasses need retrain model scratch setup dramatically reduces requirement. This survey first provides an in-depth review examining problem settings classifying prevailing methods. Moreover, we delve into applications context broad spectrum time series anomaly detection prevalent primary domains, e.g., manufacturing process monitoring, predictive maintenance, energy management, infrastructure facility monitoring. We discuss challenges limitations contexts conclude practical directions actionable suggestions address leverage diverse increasingly production environment.

Language: Английский

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Applications of artificial intelligence for energy efficiency throughout the building lifecycle: An overview

Energy and Buildings, Journal Year: 2024, Volume and Issue: 305, P. 113903 - 113903

Published: Jan. 11, 2024

Language: Английский

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Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey

Information Fusion, Journal Year: 2024, Volume and Issue: 113, P. 102601 - 102601

Published: July 27, 2024

Language: Английский

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Detection of anomaly in surveillance videos using quantum convolutional neural networks

Image and Vision Computing, Journal Year: 2023, Volume and Issue: 135, P. 104710 - 104710

Published: May 18, 2023

Language: Английский

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Intelligent fault diagnosis of bearings under small samples: A mechanism-data fusion approach

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 126, P. 107063 - 107063

Published: Sept. 1, 2023

Language: Английский

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Edge AI for Internet of Energy: Challenges and perspectives

Internet of Things, Journal Year: 2023, Volume and Issue: 25, P. 101035 - 101035

Published: Dec. 15, 2023

Language: Английский

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Parameter estimation of ECM model for Li-Ion battery using the weighted mean of vectors algorithm

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 76, P. 109891 - 109891

Published: Nov. 29, 2023

Language: Английский

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Data Science Applications in Circular Economy: Trends, Status, and Future

Environmental Science & Technology, Journal Year: 2024, Volume and Issue: 58(15), P. 6457 - 6474

Published: April 3, 2024

The circular economy (CE) aims to decouple the growth of from consumption finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due rapid development data science (DS), promising progress has been made transition toward CE past decade. DS offers various methods achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize infrastructure needed circulate materials, provide evidence-based insights. Despite exciting scientific advances this field, there still lacks a comprehensive review on topic summarize achievements, synthesize knowledge gained, navigate future research directions. In paper, we try how accelerated CE. We conducted critical where helped with focus four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency optimizing (3) extending lifetime repair, (4) facilitating reuse recycling. also introduced limitations challenges current applications discussed opportunities clear roadmap for field.

Language: Английский

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