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multilayer perceptron structure  (MathWorks Inc)


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    MathWorks Inc multilayer perceptron structure
    Multilayer Perceptron Structure, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 923 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/multilayer+structure/Deep+Learning+Toolbox/pm39682571-199-2-13
    Average 96 stars, based on 923 article reviews
    multilayer perceptron structure - by Bioz Stars, 2026-10
    96/100 stars

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    Article Title: Kinetic Oxidation Analysis in AISI 1045 Steel Using Infrared Thermography and Convolutional Neural Networks.
    Article Snippet: The SegNet model was implemented using the standard MATLAB Deep Learning Toolbox, and the architectural and training details reported in Section 2.4 facilitate reproducibility.

    Article Title: RNAGAN: Train One and Get Four, Multipurpose Human RNA-Seq Analysis Tool with Enhanced Interpretability and Small Data Size Capability
    Article Snippet: We used MATLAB 2024b Deep Learning Toolbox to establish the model and implement some customized layers (such as data-split layer for splitting the sample to diagnose and the reference data, which can also be achieved with an equivalent standard convolution layer).

    Article Title: Kinetic Oxidation Analysis in AISI 1045 Steel Using Infrared Thermography and Convolutional Neural Networks.
    Article Snippet: To implement the network, we utilised the MATLAB V2024b Deep Learning Toolbox (MathWorks, Natick, MA, USA) [21] for its flexibility in designing and training convolutional neural networks.

    Article Title: A comprehensive review of EMG/EEG based wheelchair control systems for individuals with disabilities: HMI and BCI perspectives.
    Article Snippet: Human-machine interface (HMI) and brain-computer interface (BCI) are proving to help make technologies better and helpful for people with disabilities.. These systems give individuals the ability to easily control wheelchair, and enhance their quality of life.. This review focuses on the use of EMG (muscle activity) and EEG (brain activity) signals, considered primarily as individual modalities, for wheelchair control.

    Article Title: Deep Learning-based Differentiation of Drug-induced Liver Injury and Autoimmune Hepatitis: A Pathological and Computational Approach
    Article Snippet: Training was conducted until the loss converged to a low and stable value using MATLAB’s Deep Learning Toolbox with GPU: NVIDIA GeForce RTX 3060 Ti and CPU: 13th Gen Intel Core i7-13700KF.

    Article Title: Data-driven machine learning modelling in wire EDM of TiNiCo shape memory alloy.
    Article Snippet: MATLAB’s Neural Network Toolbox (now integrated as Deep Learning Toolbox) was utilized for model construction and optimization[24].

    Article Title: Advancing bioprocess monitoring: data fusion and ANN-based prediction of arginine concentration in monoclonal antibody-producing CHO cell cultures.
    Article Snippet: The ANN models were developed and configured using the functions of MATLAB’s Deep Learning Toolbox.

    Software:

    Article Title: Advancing bioprocess monitoring: data fusion and ANN-based prediction of arginine concentration in monoclonal antibody-producing CHO cell cultures.
    Article Snippet: .. Multivariate calibration models were optimised and built in MATLAB® (MATLAB R2020a, The MathWorks, Inc.), PLS_Toolbox® 9.2 (Eigenvector Research, Inc.) chemometrics software and Deep Learning Toolbox 14.0 (The MathWorks, Inc.) was also used. ..



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    a The model constructed for molecular dynamic simulations featuring PEI polymer wetted by Al 2 O 3 layers (left), and the corresponding diffusion coefficient ( D ) of PEI polymer chains at different positions from the bottom to top Al 2 O 3 surfaces. b The diffusion coefficient of PEI polymer chains as a function of temperature. c Variation of T g for confined PEI with different film thicknesses, the thickness of the Al 2 O 3 layers is fixed at 28 nm. The error bars (±1 °C) represent the inherent error due to the fitting of the data required to obtain T g . d , e Variations of d Young’s modulus and e resistivity for PEI-Al 2 O 3 <t>nanolaminates</t> with different PEI thicknesses at various temperatures, the thickness of the Al 2 O 3 layers is fixed at 28 nm. f Dependency of breakdown strength on PEI film thickness for PEI-Al 2 O 3 nanolaminates at various temperatures, the thickness of the Al 2 O 3 layers is fixed at 28 nm.
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    a The model constructed for molecular dynamic simulations featuring PEI polymer wetted by Al 2 O 3 layers (left), and the corresponding diffusion coefficient ( D ) of PEI polymer chains at different positions from the bottom to top Al 2 O 3 surfaces. b The diffusion coefficient of PEI polymer chains as a function of temperature. c Variation of T g for confined PEI with different film thicknesses, the thickness of the Al 2 O 3 layers is fixed at 28 nm. The error bars (±1 °C) represent the inherent error due to the fitting of the data required to obtain T g . d , e Variations of d Young’s modulus and e resistivity for PEI-Al 2 O 3 <t>nanolaminates</t> with different PEI thicknesses at various temperatures, the thickness of the Al 2 O 3 layers is fixed at 28 nm. f Dependency of breakdown strength on PEI film thickness for PEI-Al 2 O 3 nanolaminates at various temperatures, the thickness of the Al 2 O 3 layers is fixed at 28 nm.
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    a The model constructed for molecular dynamic simulations featuring PEI polymer wetted by Al 2 O 3 layers (left), and the corresponding diffusion coefficient ( D ) of PEI polymer chains at different positions from the bottom to top Al 2 O 3 surfaces. b The diffusion coefficient of PEI polymer chains as a function of temperature. c Variation of T g for confined PEI with different film thicknesses, the thickness of the Al 2 O 3 layers is fixed at 28 nm. The error bars (±1 °C) represent the inherent error due to the fitting of the data required to obtain T g . d , e Variations of d Young’s modulus and e resistivity for PEI-Al 2 O 3 nanolaminates with different PEI thicknesses at various temperatures, the thickness of the Al 2 O 3 layers is fixed at 28 nm. f Dependency of breakdown strength on PEI film thickness for PEI-Al 2 O 3 nanolaminates at various temperatures, the thickness of the Al 2 O 3 layers is fixed at 28 nm.

    Journal: Nature Communications

    Article Title: High-temperature capacitive energy stroage in polymer nanocomposites through nanoconfinement

    doi: 10.1038/s41467-024-51052-y

    Figure Lengend Snippet: a The model constructed for molecular dynamic simulations featuring PEI polymer wetted by Al 2 O 3 layers (left), and the corresponding diffusion coefficient ( D ) of PEI polymer chains at different positions from the bottom to top Al 2 O 3 surfaces. b The diffusion coefficient of PEI polymer chains as a function of temperature. c Variation of T g for confined PEI with different film thicknesses, the thickness of the Al 2 O 3 layers is fixed at 28 nm. The error bars (±1 °C) represent the inherent error due to the fitting of the data required to obtain T g . d , e Variations of d Young’s modulus and e resistivity for PEI-Al 2 O 3 nanolaminates with different PEI thicknesses at various temperatures, the thickness of the Al 2 O 3 layers is fixed at 28 nm. f Dependency of breakdown strength on PEI film thickness for PEI-Al 2 O 3 nanolaminates at various temperatures, the thickness of the Al 2 O 3 layers is fixed at 28 nm.

    Article Snippet: The cross-sectional morphology of multilayer structured nanolaminates was characterized with scanning electron microscopy (JSM-7610FPlus, JEOL, Japan).

    Techniques: Construct, Polymer, Diffusion-based Assay

    a Cross-section SEM images of nanolaminates with varying layers and interlayer PEI thickness of 10 nm. The pink region represents Al 2 O 3 , and the blue region represents PEI. The scale bar is 50 nm for all. b Leakage current densities of nanolaminates with varying layers as a function of an electric field at 200 °C. c KPFM maps of the normalized contact potential difference (CPD) of nanolaminates with varying layers. From top to bottom, ~30 s, ~1 min, ~2 min, ~3 min, ~4 min, ~5 min, ~7 min, ~9 min, ~11 min after applying a 20 V voltage at the surface of the samples. The scale bar is 1 μm. d Simulated evolution of volume fraction of breakdown phase for different multilayered nanolaminates. e Failure probability of breakdown strength deduced from Weibull distribution for nanolaminates with varying layers at 25, 150, and 200 °C, respectively.

    Journal: Nature Communications

    Article Title: High-temperature capacitive energy stroage in polymer nanocomposites through nanoconfinement

    doi: 10.1038/s41467-024-51052-y

    Figure Lengend Snippet: a Cross-section SEM images of nanolaminates with varying layers and interlayer PEI thickness of 10 nm. The pink region represents Al 2 O 3 , and the blue region represents PEI. The scale bar is 50 nm for all. b Leakage current densities of nanolaminates with varying layers as a function of an electric field at 200 °C. c KPFM maps of the normalized contact potential difference (CPD) of nanolaminates with varying layers. From top to bottom, ~30 s, ~1 min, ~2 min, ~3 min, ~4 min, ~5 min, ~7 min, ~9 min, ~11 min after applying a 20 V voltage at the surface of the samples. The scale bar is 1 μm. d Simulated evolution of volume fraction of breakdown phase for different multilayered nanolaminates. e Failure probability of breakdown strength deduced from Weibull distribution for nanolaminates with varying layers at 25, 150, and 200 °C, respectively.

    Article Snippet: The cross-sectional morphology of multilayer structured nanolaminates was characterized with scanning electron microscopy (JSM-7610FPlus, JEOL, Japan).

    Techniques:

    a Discharged energy density and efficiency of nanolaminates with varying layers at temperatures of 150, 200, and 250 °C. b Comparison of maximum discharged energy density achieved at above 90% efficiency in this work and previously reported values at different temperatures. c Cyclic stability of energy density and energy efficiency for 7-layered nanolaminate under 700 kV/mm at various temperatures.

    Journal: Nature Communications

    Article Title: High-temperature capacitive energy stroage in polymer nanocomposites through nanoconfinement

    doi: 10.1038/s41467-024-51052-y

    Figure Lengend Snippet: a Discharged energy density and efficiency of nanolaminates with varying layers at temperatures of 150, 200, and 250 °C. b Comparison of maximum discharged energy density achieved at above 90% efficiency in this work and previously reported values at different temperatures. c Cyclic stability of energy density and energy efficiency for 7-layered nanolaminate under 700 kV/mm at various temperatures.

    Article Snippet: The cross-sectional morphology of multilayer structured nanolaminates was characterized with scanning electron microscopy (JSM-7610FPlus, JEOL, Japan).

    Techniques: Comparison