Update README.md

README.md

@@ -1,9 +1,39 @@
 ---
 language:
 - en
-pretty_name: engergy for induction motor simulation
+pretty_name: energy for induction motor simulation
 task_categories:
 - feature-extraction
 size_categories:
 - 10M<n<100M
----
+license: mit
+---
+
+# Dataset Card for Dataset Name
+<!-- Provide a quick summary of the dataset. -->
+This dataset is simulated for four electrical motors using simulation modeling in MATLAB.
+
+### Dataset Description
+<!-- Provide a longer summary of what this dataset is. -->
+Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. 
+## Citation [optional]
+Le T-T-H, Oktian YE, Jo U, Kim H. Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory. Sensors. 2023; 23(17):7647. https://doi.org/10.3390/s23177647
+
+**BibTeX:**
+
+@Article{s23177647,
+AUTHOR = {Le, Thi-Thu-Huong and Oktian, Yustus Eko and Jo, Uk and Kim, Howon},
+TITLE = {Time Series Electrical Motor Drives Forecasting Based on Simulation Modeling and Bidirectional Long-Short Term Memory},
+JOURNAL = {Sensors},
+VOLUME = {23},
+YEAR = {2023},
+NUMBER = {17},
+ARTICLE-NUMBER = {7647},
+URL = {https://www.mdpi.com/1424-8220/23/17/7647},
+PubMedID = {37688102},
+ISSN = {1424-8220},
+DOI = {10.3390/s23177647}
+}
+
+## Dataset Card Contact
+[email protected]