That doesn't work, it gives me data for half of my edge and afterwards it's all over the place (see attachment #2) (I guess I would need to sort out my values but why isn't it following the arc length?) Get the temperature values on my edge by using (after exporting my model from COMSOL to the COMSOL server): What I'm trying to do: Calculate the STANDARD DEVIATION of the temperature profile on that edge (for further modification of geometric parameter to optimize it). Linear Graph of a 1D Plot Group to get the temperature profile on a edge (Data Set named "Edge 2D 1") of a 2D geometry (this look like attachment #1). I could easily export my result in a text file directly from COMSOL and then do calculations in Excel but that would defeat the purpose of using my calculations results back in COMSOL! 2002.Ok, I am really confused and somewhat frustrated by how difficult it seems to read results in MATLAB from a COMSOL solution. Estimating p-values for Mardia’s coefficients of multivariate skewness and kurtosis. Test of Mardia’s coefficients of multivariate skewness and kurtosis Manera, Schouten, Becchino, Bara & Verfaillie (2010).ĭance body motion dataset Statistical test tools
Leuven communicative interaction Database. Leuven action database, Vanrie & Verfaillie, 2004. Lapenta, O.M., Xavier, A.P., Côrrea, S.C. Human biological and nonbiological point-light movements: Creation and validation of the dataset. Thomas Shipley’s lab.Ī motion-capture library for the study of identity, gender, and emotion perception by Ma, Paterson & Pollick HumanEva Dataset contains 4 subjects performing 6 common actions (e.g., walking, jogging, gesturing, etc.)ĬVonline: Action databases includes hundreds of action videos used for computer vision research and algorithm evaluation HMDB-51 includes 6766 videos covering 51 action types (Kuehne, Jhuang, Garrote, Poggio, & Serre, 2011)ĬMU Graphics Lab Motion Capture Database with six motion categories and >100 motion sequences UCF101 includes 13320 videos covering 101 action types, Soomro, Zamir & Shah, 2012. Ionescu, Papava, Olaru & Sminchisescu, 2014. Human3.6M with 3.6 million 3D human poses and corresponding images for 17 scenarios.
#Scramble data set matlab code
The toolbox was designed to allow researchers with a minimal level of MATLAB programming skills to code experiments using biological motion stimuli. We have developed the Biomotion Toolbox, which allows researchers to import motion capture data in a variety of formats, to display actions using Psychtoolbox 3, and to manipulate action displays in specific ways (e.g., invertion, 3D rotation, spatial-scrambling, phase-scrambling, and limited lifetime). However, there currently does not exist an easy-to-use tool to extract, present and manipulate motion capture data in the MATLAB environment, which many researchers use to program their experiments. In recent years, a large amount of motion capture data has become freely available online, potentially providing rich stimulus sets for biological motion research. Biomotion Toolboxīiological motion research is an increasingly active field, with a great potential to contribute to a wide range of applications, such as behavioral monitoring/motion detection in surveillance situations, intention inference in social interactions, and diagnostic tools in autism research. Journal of Experimental Psychology: General, 139(4), 702-27. Note: require MATLAB Symbolic Toolbox. Analogical and category-based inference: A theoretical integration with Bayesian causal models. Version 2: require MATLAB Symbolic Toolbox, but with faster computation Causal reasoning and Analogy
#Scramble data set matlab software
Version 1: required basic MATLAB software for simulation Bayesian generic priors for causal learning. Matlab code for simulating models of human causal induction, used in Lu, H., Yuille, A., Liljeholm, M., Cheng, P. Verbal analogy problem sets: an inventory of testing materials. Verbal Analogy Dataset: Ichien, N., Lu, H., & Holyoak, K. Proceedings of the National Academy of Sciences, 116, 4176-4181. Emergence of analogy from relation learning. MATLAB code for Lu, H., Wu, Y, & Holyoak, K.H. Bayesian analogy with relational transformations.
Probabilistic analogical mapping with semantic relation networks.
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