Repo for paper "Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information"

Last update: Jul 01, 2021

Related tags

Deep Learning EVSIvsLSTM

Overview

Repo for paper "Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information"

Notes

I probably still left some absolute path in the project. When running a script, please check the paths used for loading dataset, saving models and etc.

Repo Structure

The section contains the structure graph of this project and some simple descriptions of folders

For more detailed description of each script, please refer to the README inside each folder.
Every item with . extension is a file/script. Items without . extension are folders.
Folders like dataset, models, backup are not actually empty. But because they usually hold fairly large files/datasets I decided to not upload the content directly to github (.gitignore are left in those folders as placeholders). Please contact me directly if you need those files.

│   env.yml: environment file (under Windows 10) for Conda. Use this to generate a working environment
│
├───evsi: scripts/data related to EVSI portion of this project
│   │   get_EVSI.ipynb
│   │   get_models.ipynb
│   │   ranking_and_correlation.ipynb
│   │   sensitivity_analysis.ipynb
│   │   training_and_evsi_fs.ipynb
│   │
│   ├───backup: results of each run of `training_and_evsi_fs.ipynb`.
│   ├───dataset: raw data of the TE dataset
│   ├───log: relevant metrics generated after the current run
│   │       acc.csv
│   │       acc_improvement.csv
│   │       sensitivity_analysis.csv
│   │       sensor_selection.csv
│   │
│   └───models: frozen LSTM models saved after the current run
│       ├───evsi: models trained for EVSI purpose
│       └───ml: models trained for forward stepwise selection purpose
|
└───ml
    │   LSTM_RandomForest.ipynb
    │   LSTM_workflow.ipynb
    │   README.md
    │   visulization.ipynb
    │
    ├───dataset: raw data of the TE dataset
    ├───models: frozen LSTM models that are used to pick the top 10 impactful features
    └───plots: plots generated to demonstrate the 10 most impactful features
            test_advantage.png
            validation_advantage.png

Dataset

The dataset used in this project is the Tennessee Eastman Process Simulation Data presented here

More Info

For more information about this project, for example, the structure of the dataset, please refer to this document

Repo for paper "Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information"

Related tags

Overview

Notes

Repo Structure

Dataset

More Info

Owner

Berkeley Expert System Technologies Lab

Official repository for "Restormer: Efficient Transformer for High-Resolution Image Restoration". SOTA results for single-image motion deblurring, image deraining, image denoising (synthetic and real data), and dual-pixel defocus deblurring.

[ICLR'21] Counterfactual Generative Networks

This is a classifier which basically predicts whether there is a gun law in a state or not, depending on various things like murder rates etc.

MLP-Numpy - A simple modular implementation of Multi Layer Perceptron in pure Numpy.

Tackling Obstacle Tower Challenge using PPO & A2C combined with ICM.

🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱

A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

code for paper"A High-precision Semantic Segmentation Method Combining Adversarial Learning and Attention Mechanism"

Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

Pixray is an image generation system

An open framework for Federated Learning.

Accelerating BERT Inference for Sequence Labeling via Early-Exit

Comp445 project - Data Communications & Computer Networks

Keras implementations of Generative Adversarial Networks.

project page for VinVL

PyJokes - Joking around with Python library pyjokes

Gapmm2: gapped alignment using minimap2 (align transcripts to genome)

CAST: Character labeling in Animation using Self-supervision by Tracking

Predicting path with preference based on user demonstration using Maximum Entropy Deep Inverse Reinforcement Learning in a continuous environment

A Python wrapper for Google Tesseract