Impurity gas detection dataset on 2/3-scaled canister mock-up

This repository contains datasets used for training Variational Autoencoders (VAE) and Wasserstein Autoencoders (WAE) in the detection of impurity gases on a 2/3-scaled sealed canister mock-up. The research focuses on identifying abnormal gas concentrations using acoustic sensing techniques. These d...

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Hauptverfasser: Bozhou, Zhuang, Bora, Gencturk, Assad, Oberai, Harisankar, Ramaswamy, Ryan, Meyer, Anton, Sinkov, Morris, Good
Format: Dataset
Sprache:eng
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Zusammenfassung:This repository contains datasets used for training Variational Autoencoders (VAE) and Wasserstein Autoencoders (WAE) in the detection of impurity gases on a 2/3-scaled sealed canister mock-up. The research focuses on identifying abnormal gas concentrations using acoustic sensing techniques. These datasets were utilized in the study titled "Acoustic Sensing and Auto-encoder Approach for Abnormal Gas Detection on a Spent Nuclear Fuel Canister Mock-up." Files 1. X_train_helium(argon).npy: This file contains the training data with helium in day-1.2. Argon_Percentage.npy: This file contains the different impurity levels of argon used in the experiments.3. impurity_data.npz: This file is a dictionary containing ultrasonic response data for various argon impurity levels.4. X_train_helium(air).npy: This file contains the training data with helium in day-2.5. Air_Percentage.npy: This file contains the different impurity levels of argon used in the experiments.6. impurity_data.npz: This file is a dictionary containing ultrasonic response data for various air impurity levels. Data Structure X_train_helium(argon).npy- Shape: (1,991, 301)- Description: This array contains the training data, where:  - Each of the 1,991 rows represents a unique sample.  - The 301 columns represent the truncated ultrasonic response for each sample. X_train_helium(air).npy- Shape: (988, 301)- Description: This array contains the training data, where:  - Each of the 988 rows represents a unique sample.  - The 301 columns represent the truncated ultrasonic response for each sample. Argon_Percentage.npy or Air_Percentage.npy- Shape: (41, 1)- Description: This array contains the impurity levels of argon and air used in the experiments. There are 41 different impurity levels, each represented by a percentage in this file. impurity_data.npz- Structure: This file is a dictionary containing 40 items, each representing data for a specific argon impurity concentration level.- Description: Each item in the dictionary is structured as follows:  - Shape: (51, 301)  - Details:    - The 51 rows represent repeated ultrasonic response measurements at each impurity concentration level.    - The 301 columns represent the truncated ultrasonic response corresponding to each measurement. ContactFor any questions or issues related to this dataset, please contact the repository owner via bozhouzh@usc.edu.
DOI:10.5281/zenodo.13294535