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Motion and Motor-Current Data of a Four-Bar Linkage
No full textGeneral
A hardware prototype of a four-bar linkage was constructed to generate the presented data set. The data consists of desired input currents supplied to a servo motor and the measured resulting velocities. The mechanism is portrayed in the lab_mechanism_x.jpg images. Further details of the mechanism can be found in the section "Mechanism Setup".
For each input trajectory in the input/ folder, the experiment was performed three times. The corresponding measurement files are in the output_empty/ and output_honey/ folders identified by the extension output_xx, where xx is either 00, 01, or 02. For the measurements in the output_honey/ folder, a non-symmetrical stirrer was mounted to the mechanism and was moved through regular supermarket forest honey introducing additional viscous damping into the system. This also allows to supply higher currents for relevant amounts of time to the motor because the maximal motor velocity will not be reached as soon. For the files in the output_empty/ folder, no stirrer was mounted on the mechanism.
File Setup
The input and output files are comma-separated text files.
In the input files, the first line contains a column description (% Time [s], Prescribed Current [mA]) and the following lines indicate the input commands. In the input-command lines, the first value is a time marker in seconds, and the second value is a desired current that should be supplied to the motor from that time on until the time marker in the next line.
The output files have a column description in the first line (% Time [s], Goal Current [mA], Present Current [mA], Present Voltage [V], Present Position [rad], Present Velocity [rad/s]) and the following lines are the measurements from the servo motor. It is important to note that the servo motor only has a granularity of 2.69 mA steps for the supplied currents. Hence, the goal current will be the closest multiple of 2.69 below the desired current in the input file. The present position is denoted in rad, where the null position is with the left link in a horizontal position (parallel to the ground link) pointing to the right. The motor then actuates this link in counter-clockwise direction when viewed from the top.
Mechanism Setup
The four-bar linkage consists of aluminum blocks connected by revolute joints. The joints of the three moving links are 10 mm apart from the edges of the aluminum blocks. The lengths of the moving links are the following (with joint distances denoted in brackets):
left link / crank link: 50 mm (30 mm)
top link / coupler link: 124 mm (104 mm)
right link / rocker link: 80 mm (60 mm)
The ground link can be freely adjusted between 45 mm and 120 mm, but was fixed to 95 mm in the conducted experiments. A stirrer can be mounted on the mechanism and can be moved through a liquid introducing viscous damping into the system. A Dynamixel XH430-W350-R servo motor actuates the left link. The servo motor has a built-in controller and can be supplied with a desired current signal to enforce a moment on the left link. The motor is controlled via a C++ program running under Ubuntu 20.04. The baud rate is set to the highest admissible value of 4.5 Mb/s and the USB latency is set to 1 ms. An accelerometer (Bosch BMA456) is mounted to the top of the mechanism but has not been used in the experiments.
Python Notebook tutorial.ipynb
This Python 3 notebook visualizes the trajectories to get an intuition about the presented data set. It exemplifies how to load and extract values from the input and output files. Afterwards, it plots the input trajectories together with corresponding velocity measurements
UNITE Toolbox
No full textUNITE Toolbox
Unified diagnostic evaluation of scientific models based on information theory
The UNITE Toolbox is a Python library for incorporating Information Theory
into data analysis and modeling workflows.
The toolbox collects different methods of estimating information-theoretic quantities
in one easy-to-use Python package.
Currently, UNITE includes functions to calculate entropy H(X),
Kullback-Leibler divergence D_{KL}(p||q), and mutual information I(X; Y),
using three methods:
Kernel density-based estimation (KDE)
Binning using histograms
k-nearest neighbor-based estimation (k-NN)
DaRUS
This is the DaRUS archive for the UNITE toolbox. This archive is currently in version 0.1.9 which is the latest version as of 13.05.2024. This archive will be updated semi-regularly. Check the Github repository for the latest version.
Installation
Although the code is still highly experimental and in very active development,
a release version is available on PyPI and can be installed using pip.
pip install unite_toolbox
Check the pyproject.toml for requirements.
Note: pip will install the latest version which might not exactly match this archive.
How-to
In the documentation please find
tutorials on
the general usage of the toolbox and some applications.</p
Replication Data for: Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning
No full textScripts used in the experiments and analysis presented in the paper
ApHIN - Autoencoder-based port-Hamiltonian Identification Networks (Software Package)
No full textSoftware package for data-driven identification of latent port-Hamiltonian systems.
Abstract
Conventional physics-based modeling techniques involve high effort, e.g.~time and expert knowledge, while data-driven methods often lack interpretability, structure, and sometimes reliability. To mitigate this, we present a data-driven system identification framework that derives models in the port-Hamiltonian (pH) formulation. This formulation is suitable for multi-physical systems while guaranteeing the useful system theoretical properties of passivity and stability.
Our framework combines linear and nonlinear reduction with structured, physics-motivated system identification. In this process, high-dimensional state data obtained from possibly nonlinear systems serves as the input for an autoencoder, which then performs two tasks: (i) nonlinearly transforming and (ii) reducing this data onto a low-dimensional manifold. In the resulting latent space, a pH system is identified by considering the unknown matrix entries as weights of a neural network. The matrices strongly satisfy the pH matrix properties through Cholesky factorizations. In a joint optimization process over the loss term, the pH matrices are adjusted to match the dynamics observed by the data, while defining a linear pH system in the latent space per construction. The learned, low-dimensional pH system can describe even nonlinear systems and is rapidly computable due to its small size.
The method is exemplified by a parametric mass-spring-damper and a nonlinear pendulum example as well as the high-dimensional model of a disc brake with linear thermoelastic behavior
Features
This package implements neural networks that identify linear port-Hamiltonian systems from (potentially high-dimensional) data [1].
Autoencoders (AEs) for dimensionality reduction
pH layer to identify system matrices that fullfill the definition of a linear pH system
pHIN: identify a (parametric) low-dimensional port-Hamiltonian system directly
ApHIN: identify a (parametric) low-dimensional latent port-Hamiltonian system based on coordinate representations found using an autoencoder
Examples for the identification of linear pH systems from data
One-dimensional mass-spring-damper chain
Pendulum
discbrake model
See documentation for more details
Onco* tutorial
No full textOnco* tutorial
This repository contains a tutorial for the following software packages:
OncoFEM
OncoSTR
OncoTUM
OncoGEN
The goal is to demonstrate all functions and provide users with easier access to the tools.
After successfully installing one or all of the packages, the respective tutorial can be started with Python, for example:
python oncofem_tut_01_quick_start.py
Legacy versions can be found in the OncoFEM data repository, respectively in virtual box for OncoFEM
Data for: Formation of common preferential two-phase displacement pathways in porous media
No full textWith the use of optical microscopy, microfluidic experiments took place in quasi-2D artificial porous media for a variety of cyclic displacement processes and boundary conditions, four of which are shared here. This Dataset contains the data presented in the related publication Vahid Dastjerdi et. al. (2024).
Each experiment is flow-controlled and has a different flow rate. Three of the experiments include 12, 21, or 24 sequential displacement events. The images and pressures recorded during the experiments are presented. Due to some technical issues, pressure values for experiment 2 are not available
D1244 sensor data (November 2022 - May 2023)
No full text
General information:
This dataset contains measurements from the adaptive high-rise demonstrator building D1244, built in the scope of the CRC1244. This 36m high building is equipped with 24 hydraulic actuators providing the basis for its structural adaptation. Strain gauges, pressure sensors and position encoders are mounted throughout the building and used for state estimation and monitoring.
Structure of the dataset:
Each zip-file contains measurements of one day in the hdf5 format.
The hdf5-files in each zip-file contain an array of 244 signals sampled over 10^6 time steps at approximately 200Hz.
labels.csv contains auxiliary information on all measured signals, including the sensor type and the sensor's location in the building
File contents:
Each hdf5-file contains signals of the following types, arranged as stated in labels.csv:
strain: strain (in mm/m) in columns and diagonal bracing elements, measured by strain gauges.
pressure: pressure (in bar) in the piston side or the rod side chamber of a hydraulic actuator. For actuators in the diagonal bracing, the rod side chamber is permanently connected to the tank.
posenc: displacement (in meters) of each actuator, measured by a position encoder.
optic: optically measured displacement (in meters) of emitters attached to the building's facade.
The building consists of four modules spanning three stories each, and all sensors within a module are connected to a control cabinet, from which all measurements are transmitted. The cameras of the optical measurement system are placed outside the building and transmit their data separately from the sensors within the building. Therefore, there are an additional two signals per module (or camera):
timestamp: unix timestamp (seconds since 1st January 1970) of the control cabinet
numvars: number of measured variables
Missing measurements are marked as NaN. The optical measurement system is currently undergoing maintenance, which is why the corresponding signals are all NaN.
</p
exaFOAM Microbenchmark MB10 - ERCOFTAC conical diffuser LES
No full textThis work is part of the exaFOAM project that aims to enable the open-source CFD software OpenFOAM to exploit massively parallel HPC architectures and overcome performance scaling bottlenecks. The ERCOFTAC conical diffuser is a challenging case for turbulence modeling since it operates in the flow regime close to separation at the diffuser wall. Depending on the turbulence model the simulation may predict separation producing results, which do not correspond to the experimental ones. This is shown in the description of the RAS microbenchmark based on the low Reynolds k-omega-SST turbulence model. Here, an additional setup and corresponding microbenchmark incorporating an LES model is proposed. Detailed information and case setup can be found in the README.md file contained in the case setup file
Data for Comparison of the Catalytic Activity of Surface-Immobilized Copper Complexes with Phosphonate Anchoring Groups for Atom-Transfer Radical Cyclizations and Additions
No full textThis file contains the NMR- and UV/Vis data relevant to the publication given in the title. This data was produced in the course of our work on alumina immobilized Cu catalysts for the ATRA and ATRC reactions of halogenated organic substrates and gives proof of the reaction products and the catalyst structures both on the surface and in homogeneous solution
Synchrotron X-ray video of full-penetration laser welding of aluminum AA1050A
No full textThe video shows a synchrotron x-ray video of full-penetration laser welding of the aluminum alloy AA1050A (Al99.5). At the beginning of the video the transition from partial penetration welding with a keyhole which is closed at its bottom to full-penetration welding with a keyhole which is opened at its bottom can be observed. The images show that the fluctuations of the capillary’s geometry during the beginning of the process result in an excessive formation of pores. During the further progress of the process a reliable full-penetration process is achieved with an increased stability of the geometry of the keyhole. A detailed analysis of this transition and its implications on the absorptance are presented in the related publication