The purpose is to facilitate sharing code and data needed to customers from published technical papers from the IOTAA team. A link to ALL Technical papers published within SAS can be found at Technical Papers | SAS Support
Technical Papers linked to this repository:
- Time-Frequency Analysis Methods and Applications in SAS®
- Fault Identification Using Dynamic Bayesian Networks
- Nominal Variables Dimension Reduction Using SAS
| File/Folder | Application |
|---|---|
| Fault Identification Using Dynamic Bayesian Networks/TE.sas | Fault identification using dynamic Bayesian networks for Tennessee Eastman chemical plant process. |
| Fault Identification Using Dynamic Bayesian Networks/two_tank.sas | Fault identification using dynamic Bayesian networks for two-tank data. |
| Signal Processing Methods and Applications in SAS/Examples and Datasets/Music Decomposition with EMD and HHT | Instrument-Based Music Decomposition |
| Signal Processing Methods and Applications in SAS/Examples and Datasets/Feature Extraction from EEG using EMD | Analyzing EEG Signals |
| Signal Processing Methods and Applications in SAS/Examples and Datasets/Queen Bee Piping Part I and Part II | Queen Bee Piping Example 1 |
| Signal Processing Methods and Applications in SAS/Examples and Datasets/Queen Bee Piping Part I and Part II | Queen Bee Piping Example 2 |
| Nominal Variables' Dimension Reduction Using SAS/PROC NOMINALDR with Logistic Regression on Soybean Data | Preprocessing Soybean Data using PROC NOMINALDR for Logistic Regression |
| Nominal Variables' Dimension Reduction Using SAS/PROC NOMINALDR with Neural Network on Molecular Biology Data | Preprocessing Molecular Biology Data using PROC NOMINALDR for Neural Network |
| Nominal Variables' Dimension Reduction Using SAS/PROC NOMINALDR with Gaussian Process Classification on Mushroom Data | Preprocessing Mushroom Data using PROC NOMINALDR for Gaussian Process Classification |
| File/Folder | Application |
|---|---|
| Fault Identification Using Dynamic Bayesian Networks/TE | A folder containing data for TE.sas. Generated using PROC IML code based on code from Ricker (2002). |
| Fault Identification Using Dynamic Bayesian Networks/two_tank | A folder containing data for two_tank.sas. Adapted from Lerner et al. (2000). |
| Signal Processing Methods and Applications in SAS/Examples and Datasets/Feature Extraction from EEG using EMD/eeg.sas7bdat | Dataset used for EEG feature extraction. |
| Signal Processing Methods and Applications in SAS/Examples and Datasets/Music Decomposition with EMD and HHT | Three audio files used for the music decomposition example. The files are bass.wav, flute.wav, combo.wav |
| Signal Processing Methods and Applications in SAS/Examples and Datasets/Queen Bee Piping Part I and Part II | Three sas datasets needed to run the Queen bee piping detection examples. The datasets are fs.sas7bdat, spectral_adj.sas7bdat, and spectral_data.sas7bdat |
| Nominal Variables' Dimension Reduction Using SAS/PROC NOMINALDR with Logistic Regression on Soybean Data | A folder containing Soybean datasets for PROC NOMINALDR and PROC LOGISTIC: soybean-large.data for training and soybean-large.test for testing |
| Nominal Variables' Dimension Reduction Using SAS/PROC NOMINALDR with Neural Network on Molecular Biology Data | A folder containing Molecular Biology Datasets for PROC NOMINALDR and PROC NNET: molecularBiologyTrain.csv for training and molecularBiologyTest.csv for testing |
| Nominal Variables' Dimension Reduction Using SAS/PROC NOMINALDR with Gaussian Process Classification on Mushroom Data | A folder containing Mushroom Datasets for PROC NOMINALDR and PROC GPCLASS: mushroomTrain.csv for training and mushroomTest.csv for testing |
All code requires software that runs SAS IML and other procs in SAS such as SAS Viya. For more information please see SAS.com
No updates as of 2/13/24
Required. If you are part of IOTAA and would like to contribute to this repository, please email [email protected] to be added as a collaborator.
We welcome your contributions! Please read CONTRIBUTING.md for details on how to submit contributions to this project.
This project is licensed under the Apache 2.0 License.
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