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awesome-genetic-programming

A curated list of resources for genetic programming.

batteryml

data-driven-modelling-of-li-ion-batteries

Perform data driven modelling of Li-ion batteries using convex optimization for parametrization of RC link models from open source testing datasets.

data-driven-modelling-of-lithium-ion-batteries

Developed a data-driven prognostic model using the Long short-term memory (LSTM) algorithm to predict the state of charge (SoC) and state of health (SoH) of the lithium-ion battery where the dataset was taken from the NASA Repository. The proposed LSTM algorithm was compared against other deep learning algorithms based on RMSE value.

deep-casas-visualization

Deep learning and LSTM approaches for human activity recognition

ellyn

python-wrapped version of ellen, a linear genetic programming system for symbolic regression and classification.

fully-convolutional-network-smart-homes

generative-pretrained-embedding-and-hierarchical-representation-to-unlock-adl-rhythm-in-smart-homes

gpcbench

GP-based classifiers benchmark

har-context-and-semantic

har_wcnn

li-ionsocalgorithm_gradientboosting

The available capacity of a battery, called the state of charge, is a fundamental characteristic for energy storage applications or electric vehicles. In order to model the state of charge of a lithium-ion battery using data-driven techniques, complex algorithms should be used so the dynamic behaviours of the battery are captured. An ensemble decis

linear-regression-of-data-driven-battery

Machine-learning approach In this work, author has developed data-driven models that accurately predict the cycle life of commercial lithium iron phosphate (LFP)/ graphite cells using early-cycle data, with no prior knowledge of degradation mechanisms. To build an early-prediction model, a feature-based approach is used. Features, such as initial

multidimensional-genetic-programming-for-multiclass-classification

Multidimensional genetic programming for multiclass classification

pyadlml

Contains data preprocessing and visualization methods for ADL datasets.

python-stdgp

An easy-to-use scikit-learn inspired implementation of the Standard Genetic Programming (StdGP) algorithm.

robotic-battery-modeling

This repository illustrates how to model based on ECM and Data Driven techniques LiFePo types of battery packs used for E-mobility and Robotics applications. The main goal is to simulate the behavior of the battery during charge and discharge cycles while feeding a BLDC motor in order to study and map the SoC of the battery

sdhar-home-a-sensor-dataset-for-human-activity-recognition-at-home

Repository where the content of the SDHAR-HOME database is hosted, as well as the Python code to use it. Pending to be published.

smarthomeharlib

SmartHomeHARLib is a small library to implement, test, and evaluate Smart Home Human Activity Recognition algorithms. Many algorithms and datasets exist in the litterature. This library try to contain most as possible datasets and algorithms for research in the Human Activity Recognition (HAR) for Smart Home field.

srbench

A living benchmark framework for symbolic regression

technoeconomiccellselection

Helps you select the optimal cell to electrify a long-haul truck from a database containing 160 cells.

truckbatterydesign

Battery design method for battery-electric long-haul trucks

ultra-early-performance-prediction

Data and code for the paper "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model"

using-language-model-to-bootstrap-human-activity-recognition-in-smart-homes