Artificial Intelligence Based Statistical Process Control for Monitoring and Quality Control of Water Resources: A Complete Digital Solution
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Publication Date
Sun Sep 05 2021
Journal Name
Journal Of Arab Statistical Union (jasu)
Publication Date
Wed Jan 01 2020
Journal Name
University Of Plymouth
Publication Date
Thu Dec 30 2004
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Publication Date
Sun Aug 01 2021
Journal Name
International Journal Of Mechanical Engineering And Robotics Research
Adaptive Approximation-Based Feedback Linearization Control for a Nonlinear Smart Thin Plate
nonlinear vibrations
feedback linearization
smart plates
piezo-patches
This paper proposes feedback linearization control (FBLC) based on function approximation technique (FAT) to regulate the vibrational motion of a smart thin plate considering the effect of axial stretching. The FBLC includes designing a nonlinear control law for the stabilization of the target dynamic system while the closedloop dynamics are linear with ensured stability. The objective of the FAT is to estimate the cubic nonlinear restoring force vector using the linear parameterization of weighting and orthogonal basis function matrices. Orthogonal Chebyshev polynomials are used as strong approximators for adaptive schemes. The proposed control architecture is applied to a thin plate with a large deflection that stimulates the axial loadin
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(3)
Publication Date
Mon Apr 26 2021
Journal Name
Journal Of Electrical Engineering & Technology
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(5)
Publication Date
Sun Dec 31 2023
Journal Name
Sumer Journal For Pure Science
Publication Date
Wed Jan 01 2014
Journal Name
Siam Journal On Control And Optimization
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(22)
Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
(72)
(64)
Publication Date
Sun Oct 15 2023
Journal Name
Journal Of Yarmouk
Publication Date
Mon Jul 01 2024
Journal Name
Journal Of Food Process Engineering
Artificial intelligence‐based modeling of novel non‐thermal milk pasteurization to achieve desirable color and predict quality parameters during storage
Abstract<sec><label></label><p>This study proposed using color components as artificial intelligence (AI) input to predict milk moisture and fat contents. In this sense, an adaptive neuro‐fuzzy inference system (ANFIS) was applied to milk processed by moderate electrical field‐based non‐thermal (NP) and conventional pasteurization (CP). The differences between predicted and experimental data were not significant (<italic>p</italic> > 0.05) for lightness (<italic>L</italic>*), redness‐greenness (<italic>a</italic>*), yellowness‐blueness (<italic>b</italic>*), total color differences (∆<italic>E</italic>), hue angle (<italic>h</italic></p></sec>
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