tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2016-02-06T08:32:25-05:00261721100tag:www.mathworks.com,2005:FileInfo/510412015-05-31T16:33:48Z2016-02-06T13:09:28ZQuick Fatigue ToolStress-based high cycle fatigue analysis application<p><a href="https://www.youtube.com/watch?v=7eYRGjTVjNQ">https://www.youtube.com/watch?v=7eYRGjTVjNQ</a>
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<br />Consult the README file for common questions and quick set-up instructions
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<p>Quick Fatigue Tool for MATLAB is an experimental code for high cycle fatigue (HCF) analysis. The code was originally developed to assess the viability of Findley’s method for multiaxial fatigue, and has since been developed to cover general, stress-based HCF fatigue applications.
<br />Quick Fatigue Tool allows the user to analyse stresses from Finite Element Analysis (FEA) results. One of the main advantages of calculating fatigue lives from FEA is that it eliminates the requirement to manually compute stress concentration and notch sensitivity factors.</p>
<p>The code runs as a script within the MATLAB environment and can be used to analyse stresses from finite element models. Quick Fatigue Tool requires the following inputs from the user:</p>
<p>1. A material definition
<br />2. A loading definition consisting of stress datasets and load histories</p>
<p>The analysis is defined by a 'job' file. This is a simple MATLAB script containing a list of options which define a particular aspect of the analysis.</p>
<p>Below is a summary of the main features of Quick Fatigue Tool.</p>
<p>ESTIMATE FATIGUE LIVES FROM FINITE ELEMENT MODELS
<br />===================================================</p>
<p>Stresses exported from finite elements models can be analysed to obtain the following insights:</p>
<p>- Fatigue life
<br />- Damage
<br />- Factor of strength
<br />- Fatigue reserve factor
<br />- Maximum stress
<br />- Stress histories
<br />- Critical plane information
<br />- Many more...</p>
<p>The code includes the following fatigue analysis algorithms:</p>
<p>- Uniaxial Stress-Life
<br />- Stress-based Brown-Miller
<br />- Principal Stress
<br />- Findley's Method
<br />- von Mises
<br />- BS 7608 Fatigue of Welded Steel Joints
<br />- NASALIFE</p>
<p>The code includes several mean stress corrections to suit the needs of the analyst. It is also possible to create user-defined mean stress corrections.</p>
<p>VIEW FATIGUE RESULTS WITH ABAQUS/VIEWER
<br />=========================================</p>
<p>If the stress data sets were exported from an Abaqus ODB file, the Export Tool can be used to write field data back to the model ODB and viewed as a contour plot.</p>
<p>ESTIMATE FATIGUE LIVES OF WELDED STEEL JOINTS AND BOLTS
<br />========================================================</p>
<p>Quick Fatigue Tool includes an implementation of the British Standard 7608:1993 code of practice for fatigue design and assessment of steel structures. The user can select from ten Stress-Life curves depending on the geometry, loading and failure mode of the welded joint. The algorithm also includes Stress-Life curves for the assessment of bolted components.</p>
<p>ESTIMATE FATIGUE LIVES OF AERO ENGINE COMPONENTS
<br />===================================================</p>
<p>Quick Fatigue Tool includes a partial implementation of the NASALIFE document NASA/TM-2005-213886/REV2 for the assessment of ceramic matrix composites. The algorithm searches all possible cycle combinations in the loading to find the plane of maximum damage based on the octahedral stress.</p>
<p>ESTIMATE FATIGUE LIVES FROM MEASURED STRAIN DATA
<br />==================================================</p>
<p>Quick Fatigue Tool includes a toolbox of MATLAB apps for the analysis of measured strain data. The toolbox includes the following:</p>
<p>- Multiaxial Gauge Fatigue: Calculate fatigue lives from measured strain data
<br />- Rosette Analysis: Convert multi-channel strain gauge rosette data into principal stress and strain
<br />- Mohr's Circle Solver: Convert a stress tensor into principal stress components</p>
<p>ENJOY ACCESS TO A LARGE MATERIAL DATABASE
<br />===========================================</p>
<p>Quick Fatigue Tool includes the Material Manager app for creating and managing a material fatigue database. The database includes over 100 commonly used steels, aluminiums, irons and plastics.</p>
<p>DOCUMENTATION
<br />================</p>
<p>For a quick guide to getting started, consult the README file. For a detailed description of the code and its features, refer to the Quick Fatigue Tool User Guide.</p>Louis Vallancehttp://www.mathworks.com/matlabcentral/profile/authors/3534495-louis-vallanceMATLAB 8.3 (R2014a)falsetag:www.mathworks.com,2005:FileInfo/552782016-02-06T10:19:37Z2016-02-06T10:19:37ZCircular Object Detection LiveCircular Object Detection Live<p>Circular Object Detection Live</p>Girish Kumar Solankihttp://www.mathworks.com/matlabcentral/profile/authors/6068075-girish-kumar-solankiMATLAB 8.0 (R2012b)falsetag:www.mathworks.com,2005:FileInfo/552772016-02-06T09:40:53Z2016-02-06T09:40:53ZPhysConstA collection of fundamental physical constants in various unit systems for MATLAB.<p>See the README file in the GitHub repository for details on the syntax and supported constants.</p>Herianto Limhttp://www.mathworks.com/matlabcentral/profile/authors/1664610-herianto-limMATLAB 7.6 (R2008a)falsetag:www.mathworks.com,2005:FileInfo/552752016-02-06T03:35:47Z2016-02-06T09:37:16ZSRIMRangeDistA GUI for plotting multiple profiles of SRIM Range Distribution of Ions.<p>SRIM is a software that does Monte Carlo simulation of ion implantations. It can estimate the distribution of ions and target damages after an implantation. SRIMRangeDist helps SRIM users to plot the ion distribution data from a SRIM output with relative ease. In some occassions, SRIM users need to calculate the distribution statistics of a series of several ion implantations and do a trial-and-error work to get the optimal implantation parameters. Performing this kind of task manually in a data processing software can be a bit mundane. SRIMRangeDist allows users to plot multiple implantation profiles on the same graph, and it computes the total distribution and the 95% confidence interval of the distribution for a quick reviewing of many implantation scenarios.
<br />See the README file in the GitHub repository for explanations on how to use it.</p>Herianto Limhttp://www.mathworks.com/matlabcentral/profile/authors/1664610-herianto-limMATLAB 8.6 (R2015b)falsetag:www.mathworks.com,2005:FileInfo/552762016-02-06T07:47:05Z2016-02-06T07:47:05ZMatlab code for KEDMatlab code for KED, written by the authors for the paper published on TNNLS,2016.<p>The matlab code written by the authors for the paper: Ke-Kun Huang, Dao-Qing Dai, Chuan-Xian Ren, Zhao-Rong Lai. Learning Kernel Extended Dictionary for Face Recognition. IEEE Transactions on Neural Networks and Learning Systems, 2016, Accepted.
<br />Abstract: Sparse Representation Classifier (SRC) and Kernel Discriminant Analysis (KDA) are two successful methods for face recognition. SRC is good at dealing with occlusion while KDA does well in suppressing intra-class variations.
<br />In this paper, we propose Kernel Extended Dictionary (KED) for face recognition, which provides an efficient way for combining KDA and SRC. We first learn several kernel principal components of occlusion variations as an occlusion model, which can represent the possible occlusion variations efficiently. Then the occlusion model is projected by KDA to get the kernel extended dictionary, which can be computed via the same ``kernel trick" as new testing samples.
<br />Finally, we use structured SRC for classification, which is fast as only a small number of atoms are appended to the basic dictionary and the feature dimension is low. We also extend KED to multi-kernel space to fuse different types of features at kernel level. Experiments are done on several large-scale datasets, demonstrating that not only does KED get impressive results for non-occluded samples, but it also handles occlusion well without overfitting, even with a single gallery sample per subject.</p>Ke-Kun Huanghttp://www.mathworks.com/matlabcentral/profile/authors/5133554-ke-kun-huangMATLAB 7.14 (R2012a)MATLABPlease download CAS-PEAL dataset first: https://yunpan.cn/crC8N5ZNfGbiw, password:0c39, size: 89M.falsetag:www.mathworks.com,2005:FileInfo/324792011-08-09T15:58:58Z2016-02-06T06:27:43ZImport ImageJ ROIsRead ROIs and ROI sets saved from ImageJ into MATLAB, without java.<p>ReadImageJROI - FUNCTION Read an ImageJ ROI into a MATLAB structure
<br />Usage:
<br />[sROI] = ReadImageJROI(strFilename)
<br />[cvsROIs] = ReadImageJROI(cstrFilenames)
<br />[cvsROIs] = ReadImageJROI(strROIArchiveFilename)</p>
<p>This function reads the ImageJ binary ROI file format.</p>
<p>'strFilename' is the full path to a '.roi' file. A list of ROI files can be passed as a cell array of filenames, in 'cstrFilenames'. An ImageJ ROI archive can be access by providing a '.zip' filename in 'strROIArchiveFilename'. Single ROIs are returned as matlab structures, with variable fields depending on the ROI type. Multiple ROIs are returned as a cell array of ROI structures.</p>
<p>The field '.strName' is guaranteed to exist, and contains the ROI name (the filename minus '.roi').</p>
<p>The field '.strType' is guaranteed to exist, and defines the ROI type: {'Rectangle', 'Oval', Line', 'Polygon', 'Freehand', 'Traced', 'PolyLine', 'FreeLine', 'Angle', 'Point', 'NoROI'}.</p>
<p>The field '.vnRectBounds' is guaranteed to exist, and defines the rectangular bounds of the ROI: ['nTop', 'nLeft', 'nBottom', 'nRight'].</p>
<p>The field '.nVersion' is guaranteed to exist, and defines the version number of the ROI format.</p>
<p>Other structure fields are described in the function help text.</p>
<p>Hint:
<br />ROIs returned by this function can be converted into useful regions using the bwconncomp function, but this requires some extra work to draw each ROI into a matrix.</p>Dylan Muirhttp://www.mathworks.com/matlabcentral/profile/authors/760437-dylan-muirMATLAB 7.9 (R2009b)falsetag:www.mathworks.com,2005:FileInfo/541922015-11-27T13:22:09Z2016-02-06T06:03:46ZOceanOptics/MISCToolboxMatlab tools for oceanographic analysis focusing on the open ocean.<p>The functions available are:
<br />compute_bbp.m: Compute the particulate backscattering b_bp from the backscattering bb
<br />correct_npq.m: Correct for non photochemical quenching using Xing et al. (2012) and/or Sackmann et al. (2008)
<br />etimate_mld.m: Estimate the mixed layer depth (MLD) with one of the following method: fixed temperature threshold, fixed density threshold, variable density threshold or fixed density gradient.
<br />meshprofile.m: Interpolate data between profile (often used with scatter3m)
<br />need_npqc.m: Determine if need a non photochemical quenching correction
<br />scatter3m.m: 4D visualization with earth map (latitude, longitude, depth and measure)</p>Nilshttp://www.mathworks.com/matlabcentral/profile/authors/6044970-nilsMATLAB 8.5 (R2015a)betasw_ZHH2009.m from Xiaodong Zhang,
gsw_matlab_v3_04 from TEOS-10,
lr2.m a robust linear regression type II, can substitute it by regress() insteadfalsetag:www.mathworks.com,2005:FileInfo/478112014-09-11T08:39:47Z2016-02-06T06:01:44Zvlfeat/matconvnetMatConvNet: CNNs for MATLAB<p>Please refer to the homepage (<a href="http://www.vlfeat.org/matconvnet">http://www.vlfeat.org/matconvnet</a>) for releases, data, and documentation.
<br />MatConvNet is a MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications. It is simple, efficient (integrating MATLAB GPU support), and can run and learn state-of-the-art CNNs, similar to the ones achieving top scores in the ImageNet challenge. Several example CNNs are included to classify and encode images.
<br />An important feature of MatConvNet is making available the CNN building blocks as easy-to-use MATLAB commands. This allows prototyping new CNN architectures and learning algorithms as well as recycling fast convolution code for sliding window object detection and other applications.</p>
<p>MatConvNet is developed by a team of computer vision scientists in Oxford and other research institutions.</p>Andrea Vedaldihttp://www.mathworks.com/matlabcentral/profile/authors/1911119-andrea-vedaldiMATLAB 8.3 (R2014a)Parallel Computing ToolboxMATLABThe code has been tested on Mac OS X and Linux.
It requires a C compiler (Xcode or GCC respectively).
Compiling GPU support requires a copy of the freely-available NVIDIA developer toolkit and the Parallel MATLAB toolbox.falsetag:www.mathworks.com,2005:FileInfo/543862015-12-09T02:20:12Z2016-02-06T06:01:24ZtrajOpt (trajectory optimization library)A toolbox for Matlab, for solving continuous time trajectory optimization problems<p>A trajectory optimization library for Matlab.
<br />See <a href="https://github.com/MatthewPeterKelly/TrajOpt">https://github.com/MatthewPeterKelly/TrajOpt</a> for the most recent version.
<br />Current version supports direct collocation (trapazoid method, hermite simpson method), multiple shooting (4th-order Runge-Kutta), and global collocation (chebyshev pseudospectral collocation). All methods use fully vectorized code for fast calculations. Analytic gradients and error analysis are provided in the direct collocation methods.</p>Matthew Kellyhttp://www.mathworks.com/matlabcentral/profile/authors/2853760-matthew-kellyMATLAB 8.3 (R2014a)Optimization ToolboxMATLABNone for most functionality.
The 'chebyshev' global collocation method uses ChebFun (http://www.chebfun.org/), a Matlab toolbox for computing with orthogonal polynomials.
If you have GPOPS II installed, then you can use TrajOpt to call it.falsetag:www.mathworks.com,2005:FileInfo/470232014-06-21T11:40:25Z2016-02-06T06:00:26ZChebfun - current versionNumerical computation with functions<p>Chebfun is an open-source software system for numerical computing with functions. The mathematical basis is piecewise polynomial interpolation implemented with what we call “Chebyshev technology”. The foundations are described, with Chebfun examples, in the book Approximation Theory and Approximation Practice (L. N. Trefethen, SIAM 2013). Chebfun has extensive capabilities for dealing with linear and nonlinear differential and integral operators, and also includes continuous analogues of linear algebra notions like QR and singular value decomposition. The Chebfun2 extension works with functions of two variables defined on a rectangle in the x-y plane.
<br />Most Chebfun commands are overloads of familiar MATLAB commands — for example sum(f) computes an integral, roots(f) finds zeros, and u = L\f solves a differential equation.
<br />To get a sense of the breadth and power of Chebfun, a good place to start is by looking at our Examples (<a href="http://www.chebfun.org/examples/">http://www.chebfun.org/examples/</a>) or the introductory Guide (<a href="http://www.chebfun.org/docs/guide/">http://www.chebfun.org/docs/guide/</a>).</p>
<p>Please contact us with any questions/comments at <a href="mailto:help@chebfun.org">help@chebfun.org</a>.</p>Chebfun Teamhttp://www.mathworks.com/matlabcentral/profile/authors/1823057-chebfun-teamMATLAB 8.2 (R2013b)MATLABfalse