MRES 6th Edition and PRES 2nd Edition – Errata / Corrections
via MATLAB and Python Recipes for Earth Sciences (author: Martin H. Trauth)
via MATLAB and Python Recipes for Earth Sciences (author: Martin H. Trauth)
Wyoming_Radiosonde_Downloader_CSV_GB.m
MATLAB Central - File Exchange - rating:4.5
MATLAB Central - File Exchange - rating:4.5
How to Forecast Data Using the Econometric Modeler App in MATLAB
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via MATLAB@YouTube (author: MATLAB)
#YouTube
via MATLAB@YouTube (author: MATLAB)
YouTube
How to Forecast Data Using the Econometric Modeler App in MATLAB
Learn how to analyze and forecast time series data in MATLAB® using the Econometric Modeler app in Econometrics Toolbox™. This demonstration covers importing and visualizing time series data, separating long-term trends from short-term cycles, fitting a model…
Control Systems - University of Cape Town
MATLAB Central - File Exchange - rating:5.0
MATLAB Central - File Exchange - rating:5.0
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.
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.
📡 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);
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);
Engineering with Agentic AI: Using Skills to Improve Your Agent
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via MATLAB@YouTube (author: MATLAB)
#YouTube
via MATLAB@YouTube (author: MATLAB)
YouTube
Engineering with Agentic AI: Using Skills to Improve Your Agent
AI agents are already remarkably good at writing code, but engineering requires more than code generation. It requires domain expertise, established workflows, validation practices, and lessons learned from experienced engineers.
Learn how to use skill files…
Learn how to use skill files…
📡 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.
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.