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circuit simulation model  (MathWorks Inc)


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    MathWorks Inc circuit simulation model
    Circuit Simulation Model, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/simulated+circuit+model/pmc11437159-323-1-7
    Average 90 stars, based on 1 article reviews
    circuit simulation model - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Modeling and simulation of SEPIC converter based solar simulator circuit for accurate testing and analysis under varying solar radiation conditions
    Article Snippet: The circuit model was developed and tested under varying environmental conditions, including rapidly changing irradiance levels (600 W/m2, 1000 W/m2, 800 W/m2) at constant temperature (25 ◦C) with a modified Perturb and Observe (P&O) maximum power point tracker (MPPT) algorithm in Matlab/Simulink.

    Article Title: Enhanced Ion Mobility in Helmholtz Layer Enabling Ultrathick Electrodes
    Article Snippet: The data fitting with an equivalent circuit model is carried out in MATLAB.

    Article Title: Hybrid golden jackal and golden sine optimizer for tuning PID controllers
    Article Snippet: A circuit simulation model is designed using Simulink, as depicted in Fig. . Below are the benchmark transfer functions for a stable system, a system with large overshoot, and an unstable system: (1) Stable system 19 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} {G_1}(s) = \frac{1}{{{s^2} + s}} \end{aligned}$$\end{document} G 1 ( s ) = 1 s 2 + s (2) Large overshoot system 20 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} {G_2}(s) = \frac{{10}}{{0.04{s^3} + 0.54{s^2} + 1.5 s + 1}} \end{aligned}$$\end{document} G 2 ( s ) = 10 0.04 s 3 + 0.54 s 2 + 1.5 s + 1 (3) Unstable system 21 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} {G_3}(s) = \frac{{s + 2}}{{{s^4} + 8{s^3} + 4{s^2} - s + 0.4}} \end{aligned}$$\end{document} G 3 ( s ) = s + 2 s 4 + 8 s 3 + 4 s 2 - s + 0.4 Figure 8 PID controller simulation model. For each algorithm, the population size is 50 and the maximum number of iterations is 100.

    Article Title: A Novel Battery Less Regulated Output Scheme for a Hybrid Solar Photo Voltaic and Thermoelectric Generation System
    Article Snippet: Section 4 discusses the realization of the circuit model of the proposed system in MATLAB SIMULINK.

    Article Title: Design and implementation of an inductor based cell balancing circuit with reduced switches for Lithium-ion batteries
    Article Snippet: The equalisation circuit simulation model is constructed in MATLAB/Simulink to assess the rationality of the computed parameters.

    Sampling:

    Article Title: Knowledge-data driven sampling diagnosis algorithm for lithium batteries on electric vehicles.
    Article Snippet: The voltage is one of limited reliable information for battery management system, and the faults of voltage sampling will result in adverse effects and lead to potential risks for operation, which emphasize the importance for investigating the failure modes of voltage sampling and diagnosis algorithm.. In this article, a knowledge-data driven sampling diagnosis algorithm is established and an online intelligent diagnosis algorithm is proposed accordingly based on outlier detection with fuzzy entropy.. The fault diagnosis algorithm is established and evaluated under positive exploitation, where the knowledge-base of failure mode based on equivalent simulating models is firstly constructed.

    Battery:

    Article Title: Knowledge-data driven sampling diagnosis algorithm for lithium batteries on electric vehicles.
    Article Snippet: The voltage is one of limited reliable information for battery management system, and the faults of voltage sampling will result in adverse effects and lead to potential risks for operation, which emphasize the importance for investigating the failure modes of voltage sampling and diagnosis algorithm.. In this article, a knowledge-data driven sampling diagnosis algorithm is established and an online intelligent diagnosis algorithm is proposed accordingly based on outlier detection with fuzzy entropy.. The fault diagnosis algorithm is established and evaluated under positive exploitation, where the knowledge-base of failure mode based on equivalent simulating models is firstly constructed.

    Construct:

    Article Title: Knowledge-data driven sampling diagnosis algorithm for lithium batteries on electric vehicles.
    Article Snippet: The voltage is one of limited reliable information for battery management system, and the faults of voltage sampling will result in adverse effects and lead to potential risks for operation, which emphasize the importance for investigating the failure modes of voltage sampling and diagnosis algorithm.. In this article, a knowledge-data driven sampling diagnosis algorithm is established and an online intelligent diagnosis algorithm is proposed accordingly based on outlier detection with fuzzy entropy.. The fault diagnosis algorithm is established and evaluated under positive exploitation, where the knowledge-base of failure mode based on equivalent simulating models is firstly constructed.

    Article Title: Characterization of Battery‐Powered Portable Ar Plasma Jets Contacting Human Impedance Model and Its Safety Assessment for Direct Human Treatment
    Article Snippet: A portable Ar plasma jet (size: 347 × 300 × 145mm, weight: 6 kg including the gas bottle) powered by a battery for direct human treatment is developed, and the discharge characteristics are investigated with the human equivalent circuit model in this study.. The root‐mean‐square value and the specific single pulse energy of the discharge current are calculated as a combination to estimate human contact safety from the perspectives of average effects and instantaneous effects.. The equivalent circuit model built at the end figures out the essential parameter, plasma plume resistance, which mainly affects the electrical safety for human contact.

    Injection:

    Article Title: Knowledge-data driven sampling diagnosis algorithm for lithium batteries on electric vehicles.
    Article Snippet: The voltage is one of limited reliable information for battery management system, and the faults of voltage sampling will result in adverse effects and lead to potential risks for operation, which emphasize the importance for investigating the failure modes of voltage sampling and diagnosis algorithm.. In this article, a knowledge-data driven sampling diagnosis algorithm is established and an online intelligent diagnosis algorithm is proposed accordingly based on outlier detection with fuzzy entropy.. The fault diagnosis algorithm is established and evaluated under positive exploitation, where the knowledge-base of failure mode based on equivalent simulating models is firstly constructed.



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    a – c Normalized vibration velocities of all cells within one single element characterized by equivalent circuit model (EQC) simulations, COMSOL simulations and laser Doppler velocimetry (LDV) measurements. a Equivalent circuit model (EQC) simulation results. b Finite element method (FEM) simulation results. c Laser Doppler velocimetry (LDV) measurements. d Acoustic transmission efficiency characterized by equivalent circuit model (EQC) simulations. e Acoustic transmission efficiency characterized by hydrophone experiments. f Comparison of the 2D pressure field between the equivalent circuit model (EQC) and finite element method (FEM) simulations

    Journal: Microsystems & Nanoengineering

    Article Title: A low-voltage-driven MEMS ultrasonic phased-array transducer for fast 3D volumetric imaging

    doi: 10.1038/s41378-024-00755-9

    Figure Lengend Snippet: a – c Normalized vibration velocities of all cells within one single element characterized by equivalent circuit model (EQC) simulations, COMSOL simulations and laser Doppler velocimetry (LDV) measurements. a Equivalent circuit model (EQC) simulation results. b Finite element method (FEM) simulation results. c Laser Doppler velocimetry (LDV) measurements. d Acoustic transmission efficiency characterized by equivalent circuit model (EQC) simulations. e Acoustic transmission efficiency characterized by hydrophone experiments. f Comparison of the 2D pressure field between the equivalent circuit model (EQC) and finite element method (FEM) simulations

    Article Snippet: Fig. 4 The vibration velocity and two-dimensional pressure field distribution of the single element. a – c Normalized vibration velocities of all cells within one single element characterized by equivalent circuit model (EQC) simulations, COMSOL simulations and laser Doppler velocimetry (LDV) measurements. a Equivalent circuit model (EQC) simulation results. b Finite element method (FEM) simulation results. c Laser Doppler velocimetry (LDV) measurements. d Acoustic transmission efficiency characterized by equivalent circuit model (EQC) simulations. e Acoustic transmission efficiency characterized by hydrophone experiments. f Comparison of the 2D pressure field between the equivalent circuit model (EQC) and finite element method (FEM) simulations In acoustic output characterization of one single element, the axial pressures at 5 mm from the pMUT surface evaluated by the EQC model (Fig. d_i and D_ii) are consistent with the reference data acquired by hydrophone experiments (Fig. e_i and e_ii).

    Techniques: Transmission Assay, Comparison

    Results of the transmission and receiving experiments of a single element

    Journal: Microsystems & Nanoengineering

    Article Title: A low-voltage-driven MEMS ultrasonic phased-array transducer for fast 3D volumetric imaging

    doi: 10.1038/s41378-024-00755-9

    Figure Lengend Snippet: Results of the transmission and receiving experiments of a single element

    Article Snippet: Fig. 4 The vibration velocity and two-dimensional pressure field distribution of the single element. a – c Normalized vibration velocities of all cells within one single element characterized by equivalent circuit model (EQC) simulations, COMSOL simulations and laser Doppler velocimetry (LDV) measurements. a Equivalent circuit model (EQC) simulation results. b Finite element method (FEM) simulation results. c Laser Doppler velocimetry (LDV) measurements. d Acoustic transmission efficiency characterized by equivalent circuit model (EQC) simulations. e Acoustic transmission efficiency characterized by hydrophone experiments. f Comparison of the 2D pressure field between the equivalent circuit model (EQC) and finite element method (FEM) simulations In acoustic output characterization of one single element, the axial pressures at 5 mm from the pMUT surface evaluated by the EQC model (Fig. d_i and D_ii) are consistent with the reference data acquired by hydrophone experiments (Fig. e_i and e_ii).

    Techniques: Transmission Assay

    a , b Acoustic coupling effects from central excitation and edge excitation characterized by the equivalent circuit model (EQC) model and the FEM model. a Cross-talk analysis of the 3 × 3 array. ( i ) An array using central excitation at element D4, in which neighboring elements B1, B3, and C3 are characterized. ( ii ) An array using edge excitation at element A2, in which neighboring elements A1, A3, and B2 are characterized. ( iii ) Quantitative analysis results for the cross-talk degree (in dB) using the equivalent circuit model (EQC) model and the FEM model. b Cross-talk analysis of the 8 × 8 array. ( i ) An array actuated with central excitation D4, in which neighboring elements C4, E4, and D5 are characterized. ( ii ) An array actuated with edge excitation B1, in which neighboring elements A1, C1, and B2 are characterized. ( iii ). Quantitative analysis results for the cross-talk degree (in dB) using the equivalent circuit model (EQC) model and the FEM model. c – f Varied focusing intensities of the 8 × 8 MEMS phased-array transducer characterized by equivalent circuit model (EQC) simulations and experimental measurements. c Focused pressure at different depths: Comparison between the calculated results of the equivalent circuit (EQC) model and the experimental measurements. d Spatial pressure field distribution of the entire array calculated by the equivalent circuit (EQC) model. e Relationship between the excitation voltage amplitude set in the imaging platform and the focused pressure measured at 30 mm. f Relationship between the actual voltage amplitude on the device and the focused pressure measured at 30 mm

    Journal: Microsystems & Nanoengineering

    Article Title: A low-voltage-driven MEMS ultrasonic phased-array transducer for fast 3D volumetric imaging

    doi: 10.1038/s41378-024-00755-9

    Figure Lengend Snippet: a , b Acoustic coupling effects from central excitation and edge excitation characterized by the equivalent circuit model (EQC) model and the FEM model. a Cross-talk analysis of the 3 × 3 array. ( i ) An array using central excitation at element D4, in which neighboring elements B1, B3, and C3 are characterized. ( ii ) An array using edge excitation at element A2, in which neighboring elements A1, A3, and B2 are characterized. ( iii ) Quantitative analysis results for the cross-talk degree (in dB) using the equivalent circuit model (EQC) model and the FEM model. b Cross-talk analysis of the 8 × 8 array. ( i ) An array actuated with central excitation D4, in which neighboring elements C4, E4, and D5 are characterized. ( ii ) An array actuated with edge excitation B1, in which neighboring elements A1, C1, and B2 are characterized. ( iii ). Quantitative analysis results for the cross-talk degree (in dB) using the equivalent circuit model (EQC) model and the FEM model. c – f Varied focusing intensities of the 8 × 8 MEMS phased-array transducer characterized by equivalent circuit model (EQC) simulations and experimental measurements. c Focused pressure at different depths: Comparison between the calculated results of the equivalent circuit (EQC) model and the experimental measurements. d Spatial pressure field distribution of the entire array calculated by the equivalent circuit (EQC) model. e Relationship between the excitation voltage amplitude set in the imaging platform and the focused pressure measured at 30 mm. f Relationship between the actual voltage amplitude on the device and the focused pressure measured at 30 mm

    Article Snippet: Fig. 4 The vibration velocity and two-dimensional pressure field distribution of the single element. a – c Normalized vibration velocities of all cells within one single element characterized by equivalent circuit model (EQC) simulations, COMSOL simulations and laser Doppler velocimetry (LDV) measurements. a Equivalent circuit model (EQC) simulation results. b Finite element method (FEM) simulation results. c Laser Doppler velocimetry (LDV) measurements. d Acoustic transmission efficiency characterized by equivalent circuit model (EQC) simulations. e Acoustic transmission efficiency characterized by hydrophone experiments. f Comparison of the 2D pressure field between the equivalent circuit model (EQC) and finite element method (FEM) simulations In acoustic output characterization of one single element, the axial pressures at 5 mm from the pMUT surface evaluated by the EQC model (Fig. d_i and D_ii) are consistent with the reference data acquired by hydrophone experiments (Fig. e_i and e_ii).

    Techniques: Comparison, Imaging