Reinforcement learning

Yatawatta, S.

doi:10.1016/j.ascom.2024.100833

Astronomy and Computing

2024

DOI: 10.1016/j.ascom.2024.100833

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Reinforcement learning

S. Yatawatta

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Cited by 3 publications

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Deep Learning Methods in Soft Robotics: Architectures and Applications

Čakurda,

Trojanová,

Pomin

et al. 2024

Advanced Intelligent Systems

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The area of soft robotics has been subject to intense research efforts in the past two decades and constitutes a paradigm for advanced machine design in future robotic applications. However, standard methods for industrial robotics may be difficult to apply when analyzing soft robots. Deep learning, which has undergone rapid and transformative advancements in recent years, offers a set of powerful tools for analyzing and designing complex soft machines capable of operating in unstructured environments and interacting with humans and objects in a delicate manner. This review summarizes the most important state‐of‐the‐art deep learning architectures classified under supervised, unsupervised, semisupervised, and reinforcement learning scenarios and discusses their main features and benefits for different soft robotic applications, including soft robot manipulators, soft grippers, soft sensors, and e‐skins, as well as bioinspired soft robots. Specific properties of recent deep learning architectures and the usefulness of their features in addressing various types of issues found in soft robotics are analyzed. The existing challenges and future prospects are identified and discussed in view of the enhanced integration of both areas, which improves the performance of next‐generation soft machines operating in real‐world conditions.

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Deep Learning Methods in Soft Robotics: Architectures and Applications

Čakurda,

Trojanová,

Pomin

et al. 2024

Advanced Intelligent Systems

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The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques

Canché-Cab,

San-Pedro,

Ali

et al. 2024

Artif Intell Rev

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Atmospheric boundary layer (ABL) structure and dynamics are important aspects to consider in human health. The ABL is characterized by a high degree of spatial and temporal variability that hinders their understanding. This paper aims to provide a comprehensive overview of machine learning (ML) methodologies, encompassing deep learning and ensemble approaches, within the scope of ABL research. The goal is to highlight the challenges and opportunities of using ML in turbulence modeling and parameterization in areas such as atmospheric pollution, meteorology, and renewable energy. The review emphasizes the validation of results to ensure their reliability and applicability. ML has proven to be a valuable tool for understanding and predicting how ABL spatial and seasonal variability affects pollutant dispersion and public health. In addition, it has been demonstrated that ML can be used to estimate several variables and parameters, such as ABL height, making it a promising approach to enhance air quality management and urban planning.

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Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches

Aytaç,

Khanzada,

Ibrahim

et al. 2024

Membranes

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Membrane engineering is a complex field involving the development of the most suitable membrane process for specific purposes and dealing with the design and operation of membrane technologies. This study analyzed 1424 articles on reverse osmosis (RO) membrane engineering from the Scopus database to provide guidance for future studies. The results show that since the first article was published in 1964, the domain has gained popularity, especially since 2009. Thin-film composite (TFC) polymeric material has been the primary focus of RO membrane experts, with 550 articles published on this topic. The use of nanomaterials and polymers in membrane engineering is also high, with 821 articles. Common problems such as fouling, biofouling, and scaling have been the center of work dedication, with 324 articles published on these issues. Wang J. is the leader in the number of published articles (73), while Gao C. is the leader in other metrics. Journal of Membrane Science is the most preferred source for the publication of RO membrane engineering and related technologies. Author social networks analysis shows that there are five core clusters, and the dominant cluster have 4 researchers. The analysis of sentiment, subjectivity, and emotion indicates that abstracts are positively perceived, objectively written, and emotionally neutral.

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Reinforcement learning

Cited by 3 publications

References 19 publications

Deep Learning Methods in Soft Robotics: Architectures and Applications

Deep Learning Methods in Soft Robotics: Architectures and Applications

The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques

Reverse Osmosis Membrane Engineering: Multidirectional Analysis Using Bibliometric, Machine Learning, Data, and Text Mining Approaches

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