Name: Sohag Kumar Saha
Type: User
Company: Tennessee Tech University
Bio: Ph.D. Student in Electrical and Computer Engineering, Tennessee Tech University, TN, USA-38501
Location: Cookeville
Blog: https://www.sohag.net
Sohag Kumar Saha's Projects
Companion software for Introduction to Radar Using Python and MATLAB
Apply Federated Learning and Deep Learning (Deep Auto-encoder) to detect abnormal data for IoT devices.
A beautiful, simple, clean, and responsive Jekyll theme for academics
š Advice and resources for thriving and surviving graduate school
This repository accompanies the book Core Concepts and Methods in Load Forecasting.
Short-Term Solar Forecasting Using LSTMs
Wind Power Forecasting Based on Hybrid CEEMDAN-EWT Deep Learning Method
CEEMDAN-VMD-LSTM Forecasting model (a light version of CEEMDAN_LSTM)
A MATLAB package for CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)
CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.
A complete computer science study plan to become a software engineer.
# control-system-design Design robust control system in Matlab (programming+Simulink). Key themes of application: (1) Power System (2) Vehicle Propulsion (3) Distributed Generating Unit Control (4) Control of Cyber-Physical System (5) Smart Grid Controlling. ## Key Controller ## (1) Proportional-Integral-Derivative (PID) (2) Linear Quadratic Regu
Time Series
Answer keys for course - Data Analysis with Python by IBM on Coursera
Collection of useful data science topics along with articles, videos, and code
MATLAB based Implementation of Distributed optimization and load flow for MVDC systems in high integration of Renewables
EB1A Full Application - I-140 and I-485
This project aims to predict the hourly electricity load in Toronto based on the loads of previous 23 hours using LSTM recurrent neural network.
Electric Power Hardware-in-the-loop Controls Collaborative
Ettercap Project
WordPress Theme for ucf.edu/faculty
This repository will have pre-processed dataset and related scripts for building Machine learning based model for classification of False Data Injection Attack.