Design and Test of Pneumatic Artificial Muscle
Bibliographic record
Abstract
Description: This supplementary materials file contains:-All raw data generated in the experiments. -All MATLAB files required to update the ForceSight application with new data.-A video of the test procedure for a contraction force measurement of an example actuator.-License files for BioRender.com Size: Less than 250 MB Platform: MATLAB Environment: Microsoft, Linux, MacOS Major component description:-Files named "AllRegressionModelData_V1.0_XX%" are the trained neural networks with XX% of the data set aside for validation.-"ForceSight_AppDesignerFile" is the MATLAB app design file that is used to make the user interface for the tool.-"ForceSightInstaller1.0_web.exe" is the executable file to donload the compiled ForceSight tool.-"Contraction Force Data [COMPLETE]" contains all of the contraction force data in excel file format.-"Blocked Force Data [COMPLETE]" contains all of the blocked force data in excel file format.-"PAM Test Procedure"is a video of the testing procedure.-"RawData.mat" is the all of the raw blocked force data in MATLAB table format.-"WideNeuralNet_Model" is the packaged wide neural net used to create the forcesight application in MATLAB.-"Licenses" folder contains the BioRender Licenses.Detailed setup instructions: RegressionApp Executable 1. Prerequisites for Deployment Verify that MATLAB Runtime(R2023a) is installed. If not, you can run the MATLAB Runtime installer.To find its location, enter >>mcrinstaller at the MATLAB prompt.NOTE: You will need administrator rights to run the MATLAB Runtime installer. Alternatively, download and install the Windows version of the MATLAB Runtime for R2023a from the following link on the MathWorks website: https://www.mathworks.com/products/compiler/mcr/index.html For more information about the MATLAB Runtime and the MATLAB Runtime installer, see "Distribute Applications" in the MATLAB Compiler documentation in the MathWorks Documentation Center. 2. Files to Deploy and Package Files to Package for Standalone ================================-RegressionApp.exe-MCRInstaller.exe Note: if end users are unable to download the MATLAB Runtime using the instructions in the previous section, include it when building your component by clicking the "Runtime included in package" link in the Deployment Tool.-This readme file 3. Definitions For information on deployment terminology, go tohttps://www.mathworks.com/help and select MATLAB Compiler >Getting Started > About Application Deployment >Deployment Product Terms in the MathWorks DocumentationCenter. Detailed run instructions:-to add data to the model simply combine it with the RawData.mat file and re-run the regression learner application with your desired parameters. Output description:-The regression learner ap will output a .mat file for the model that can replace the "WideNeuralNet_Model.mat" file and be integrated into the "ForceSight_AppDesignerFile.mlapp" Contact Information: Jordan Savage: ja2savag@uwaterloo.ca
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".