The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. Source: http://yann.lecun.com/exdb/mnist/ Image Source: https://en.wikipedia.org/wiki/MNIST_database#/media/File:MnistExamples.png

Mudestreda Multimodal Device State Recognition Dataset obtained from real industrial milling device with Time Series and Image Data for Classification, Regression, Anomaly Detection, Remaining Useful Life (RUL) estimation, Signal Drift measurement, Zero Shot Flank Took Wear, and Feature Engineering purposes. The official dataset used in the paper "Multimodal Isotropic Neural Architecture with Patch Embedding" ICONIP23. Official Minape repository: https://github.com/hubtru/Minape Official Mudestreda dataset: https://zenodo.org/records/8238653 Conference paper: https://link.springer.com/chapter/10.1007/978-981-99-8079-6_14 Mudestreda (MD) | Size 512 Samples (Instances, Observations)| Modalities 4 | Classes 3 | Future research: Regression, Remaining Useful Life (RUL) estimation, Signal Drift detection, Anomaly Detection, Multivariate Time Series Prediction, and Feature Engineering. ## Overview * Task: Uni/Multi-Modal Classification * Domain: Industrial Flank Tool Wear of the Milling Machine * Input (sample): 4 Images: 1 Tool Image, 3 Spectrograms (X, Y, Z axis) * Output: Machine state classes: Sharp, Used, Dulled * Evaluation: Accuracies, Precision, Recal, F1-score, ROC curve * Each tool's wear is categorized sequentially: Sharp → Used → Dulled. * The dataset includes measurements from ten tools: T1 to T10. * Data splitting options include random or chronological distribution, without shuffling. * Options: * Original data or Augmented data * Random distribution or Tool Distribution ## Use Cases: | Input | Model | Output | |-----------------|---------------|-----------------------| | Use Cases: | | | | 4 Images (1 Tool Image, 3 Spectrograms (X, Y, Z)) | Classification Model | Class (Flank Tool Wear: Sharp, Used, Dulled) | | 3 Spectrograms (X,Y,Z axis) | Classification Model | Class (Flank Tool Wear) | | 1 Tool Image | Classification Model | Image Class (Flank Tool Wear) | | Future Work: | | | | [1, ..., 4] Images | Model | Remaining Useful Life (RUL) estimation | | [1, ..., 4] Images | Monitoring Model | Fault and Anomaly Detection | | [1, ..., 4] Images | Forecasting Model | Multivariate Time Series Prediction | | [1, ..., 3] Spectrograms | Model | Signal Drift measurement | | [1, ..., 4] Images | Regression Model | Zero-Shot Flank Tool Wear (in µm, 10e-6 meter) | | [1, ..., 4] Images | Feature Engineering | Diagnostic Feature Designer If you use Mudestreda dataset cite the work Minape @ ICONIP2023. ## Cite the Paper: If you reference the papr or you use Mudestreda dataset cite the work Minape @ ICONIP2023 @inproceedings{truchan2023multimodal, title={Multimodal Isotropic Neural Architecture with Patch Embedding}, author={Truchan, Hubert and Naumov, Evgenii and Abedin, Rezaul and Palmer, Gregory and Ahmadi, Zahra}, booktitle={International Conference on Neural Information Processing}, pages={173--187}, year={2023}, organization={Springer} }