M
Meftahul Ferdaus
Researcher at University of New South Wales
Publications - 45
Citations - 644
Meftahul Ferdaus is an academic researcher from University of New South Wales. The author has contributed to research in topics: Fuzzy logic & Control theory. The author has an hindex of 12, co-authored 37 publications receiving 430 citations. Previous affiliations of Meftahul Ferdaus include Agency for Science, Technology and Research & International Islamic University Malaysia.
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A state of art on magneto-rheological materials and their potential applications:
TL;DR: A recent progressive review on magneto-rheological materials technology is presented in this paper, focusing on numerous application devices and systems utilizing magneto rheology materials, including fluids, foams, grease, elastomers, and plastomers.
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A review of advances in magnetorheological dampers: their design optimization and applications
TL;DR: The advantages of these semi-active systems over passive and active systems, the versatile application of MR dampers, and the fabrication of the configurations of various MR damper models are reviewed, and an overview ofVarious MR damper models are provided.
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Advancement in energy harvesting magneto-rheological fluid damper: A review
TL;DR: A comprehensive review of the present literature on energy generated magnetorheological (MR) fluid based damper, modeling and applications of the MR damper are presented in this paper, where the basic of MR fluid and their different modes, consequences with different types of MR fluids based devices, and their relevant applications are presented.
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PALM: An Incremental Construction of Hyperplanes for Data Stream Regression
TL;DR: In this article, a parsimonious learning machine (PALM) is proposed for self-adaptive neuro-fuzzy systems (SANFS) characterized by the single-pass learning mode and the open structure property that enables effective handling of fast and rapidly changing natures of data streams.
Posted Content
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression
TL;DR: A novel SANFS, namely parsimonious learning machine (PALM), is proposed that features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering, which significantly reduces the number of network parameters because it has no rule premise parameters.