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📡 MATLAB Central - File Exchange - rating:5.0 | Systems-Level Microgrid Simulation from One-Line Diagram Simscape Power Systems can be used to schematically represent a one-line microgrid diagram using blocks that represent different distributed energy resources (DERs). The DERs in this example include renewables, such as solar, a diesel GenSet, and an energy storage system (ESS). Using the simple microgrid, you see how desktop simulation can be used to subject the distribution system with residential load changes or unintentional islanding of the microgrid. The included slides detail other common workflows for systems-level microgrid simulation. Using Simulink Real-time, this simple microgrid can quickly be migrated to a real-time machine for hardware-in-the-loop testing.

📡 MATLAB Central - File Exchange - rating:5.0 | dec2hex_ex Matlab's dec2hex converts a vector of decimals into a charachter array - which is cumbersom to manipulatedec2hex_ex gives a solution for this...while... out = dec2hex([1 10 20]) out = 3×2 char array '01' '0A' '14' Syntax: [out,outCell] = dec2hex_ex(dec_vector,numDigits,addHexPrefix) Inputs: dec_vector: hold 1 or more decimals numDigits : optional, integer, can be given as empty braces ([]) addHexPrefix: optional, default = false, if true: adds '0x' so output could be sent to 'eval' function Outputs: out : The trivial output as char array outCell : Cell arrayoutput Examples: out = dec2hex_ex([1 10 20]) % out = '[1,A,14]' out = dec2hex_ex([1 10 20],3) % out = '[001, 00A, 014]' out = dec2hex_ex([1 10 20],[], 1) % out = '[0x1,0xA,0x14]' [out, outCell] = dec2hex_ex([1 10 20]) % outCell = {'1','A','14'} [out, outCell] = dec2hex_ex([1 10 20],[], 1) % outCell = {'0x1','0xA','0x14'} [out, outCell] = dec2hex_ex([1 10 20],4 , 1) % out = '[0x0001, 0x000A, 0x0014]'

📡 MATLAB Central - File Exchange - rating:4.5 | Deep Learning Tutorial Series The code provides hands-on examples to implement convolutional neural networks (CNNs) for object recognition. The three demos have associated instructional videos that will allow for a complete tutorial experience to understand and implement deep learning techniques. The demos include:- Training a neural network from scratch- Using a pre-trained model (transfer learning)- Using a neural network as a feature extractor The use of a GPU and Parallel Computing Toolbox™ is recommended when running the examples. Demo 3 requires Statistics and Machine Learning Toolbox™ in addition to the required products below.

JOSS：Macapype: An open multi-software framework for non-human primate brain anatomical MRI processing

Agentic AI for RF System Design #YouTube via MATLAB@YouTube (author: MATLAB)

📡 MATLAB Central - File Exchange - rating:5.0 | CATT2Matlab More info in GitHub, clic on 'Learn more'.I'm developing the information, sorry, soon it will be more complete.Work in Windows and OSX. Screenshoots in GitHub.

What Is Econometrics Toolbox? #YouTube via MATLAB@YouTube (author: MATLAB)

Engineering with Agentic AI: Using Skills to Improve Your Agent #YouTube via MATLAB@YouTube (author: MATLAB)

The All-Electric Flying Machine Reinventing Sea Travel #YouTube via MATLAB@YouTube (author: MATLAB)

📡 MATLAB Central - File Exchange - rating:5.0 | truncAxis (break X/Y axis | 截断坐标轴) Basic usagetruncAxis(gca, 'X', [x1, x2])truncAxis(gca, 'Y', [y1, y2])truncAxis(gca, 'X', [x1, x2], 'Y', [y1, y2]) Where:x1, x2: break points on X-axis, satisfying XLim(1) < x1 < x2 < XLim(2)y1, y2: break points on Y-axis, satisfying YLim(1) < y1 < y2 < YLim(2)Demo 1X = linspace(0, 2*pi, 100);Y = exp(.3*X).*sin(3*X);scatter(X, Y, 'LineWidth',1);set(gca, 'Box','on', 'NextPlot','add', 'LineWidth',1.5, ... 'FontName','Times New Roman', 'FontSize',15, ... 'XLim',[0, 6], 'YLim',[-6, 6], 'XColor','r','YColor','b', ... 'XMinorTick','on', 'YMinorTick','on', 'TickDir','out', 'TickLength',[.005,.001]);truncAxis('X',[4, 4.5], 'Y',[2, 3], 'XResize','on', 'YResize','on')legendDemo 2% Generate sample dataData = [randi([20, 35], [5, 1]), randi([160, 190], [5, 1])];err = rand([5, 2]) .* 5;hold onbarHdl = bar(Data, 'BarWidth', 1, 'LineWidth',1);% Customize bar colors and transparencybarHdl(1).FaceColor = [153, 153, 253] ./ 255;barHdl(2).FaceColor = [255, 153, 154] ./ 255;barHdl(1).FaceAlpha = 0.9;barHdl(2).FaceAlpha = 0.9;% Render and customize error barserrorbar(barHdl(1).XEndPoints, Data(:, 1), err(:, 1), ... 'LineStyle', 'none', 'Color', 'k', 'LineWidth', 1);errorbar(barHdl(2).XEndPoints, Data(:, 2), err(:, 2), ... 'LineStyle', 'none', 'Color', 'k', 'LineWidth', 1);% Axes styling and label customizationax = gca;ax.YLim = [0, 200];ax.LineWidth = 1;ax.TickLength = [0.005, 0.001];ax.Box = 'on';ax.FontSize = 13;ax.XTick = 1:5;ax.XTickLabel = {'A', 'B', 'C', 'D', 'E'};axMat = truncAxis('Y', [50, 155]);axMat(1).Children(end).BaseLine.Color = 'none';% Add legendlegend({'AAAAA', 'BBBBB'}, 'FontSize', 13);

Electrolyzer modelling toolbox MATLAB Central - File Exchange - rating:5.0 (RSS) [](https://se.mathworks.com/matlabcentral/fileexchange/117175-electrolyzer-modelling-toolbox)### GENERAL DESCRIPTIONThis toolbox is a Matlab tool for electrolysis modelling, for both PEM and alkaline systems. Main functionality is in parametrization of the UI curvebased on measured data. This toolbox aims to simplify the process and provide easy-to-use commands for quickly determining the UI curve parameter values and their margins of uncertainty.Since version 2.0.0, dynamic modeling functionality has been added, being capable of fitting model parameters from time-dependent data sets and using the fit parameters to simulate dynamic voltage response to arbitrarycurrent waveforms.---### INSTALLATIONMATLAB needs to be installed on the computer for the user to be able toinstall the electrolyzer modelling toolbox. Following steps install thetoolbox for that matlab installation:1. Download the provided electrolyzerModellingToolbox.mltbx file from the releases page. - Double click on the .mltbx file to initiate installation.2. Once the installation completes the toolbox functions are usable in all matlab projects---### AUTHORS AND CONTACTSPietari PuranenJunior researcher at the Lappeenranta-Lahti University of Technology LUTEmail: Pietari.Puranen@lut.fiLauri JärvinenJunior researcher at the Lappeenranta-Lahti University of Technology LUTEmail: Lauri.Jarvinen@lut.fi---### AKNOWLEDGEMENTSMarchov Chain Monte Carlo (MCMC) tools provided by Marko Laine have been used for the determination of uncertainty in curve fitting when using the particleswarm optimization method. The licensing for the tools can be foundunder ./Utils/mcmcstat/LICENSE.txtThe Academy of Finland is acknowledged for the main financial support ofthe Research of Power Quality Effect on Water Electrolyzer Operation (POQELYZER) project.

What Is Polyspace Code Prover? #YouTube via MATLAB@YouTube (author: MATLAB)

Control Systems - University of Cape Town MATLAB Central - File Exchange - rating:5.0

How to Forecast Data Using the Econometric Modeler App in MATLAB #YouTube via MATLAB@YouTube (author: MATLAB)

Wyoming_Radiosonde_Downloader_CSV_GB.m MATLAB Central - File Exchange - rating:4.5

pinn-toolbox MATLAB Central - File Exchange - rating:5.0