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

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

Owner
Berkeley Expert System Technologies Lab
Berkeley Expert System Technologies Lab
Tutorial page of the Climate Hack, the greatest hackathon ever

Tutorial page of the Climate Hack, the greatest hackathon ever

UCL Artificial Intelligence Society 12 Jul 02, 2022
A spatial genome aligner for analyzing multiplexed DNA-FISH imaging data.

jie jie is a spatial genome aligner. This package parses true chromatin imaging signal from noise by aligning signals to a reference DNA polymer model

Bojing Jia 9 Sep 29, 2022
Autonomous Movement from Simultaneous Localization and Mapping

Autonomous Movement from Simultaneous Localization and Mapping About us Built by a group of Clarkson University students with the help from Professor

14 Nov 07, 2022
PyTorch framework, for reproducing experiments from the paper Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. Code, based on the PyTorch framework, for reprodu

Asaf 3 Dec 27, 2022
VIsually-Pivoted Audio and(N) Text

VIP-ANT: VIsually-Pivoted Audio and(N) Text Code for the paper Connecting the Dots between Audio and Text without Parallel Data through Visual Knowled

Yän.PnG 16 Nov 04, 2022
The full training script for Enformer (Tensorflow Sonnet) on TPU clusters

Enformer TPU training script (wip) The full training script for Enformer (Tensorflow Sonnet) on TPU clusters, in an effort to migrate the model to pyt

Phil Wang 10 Oct 19, 2022
Qt-GUI implementation of the YOLOv5 algorithm (ver.6 and ver.5)

YOLOv5-GUI 🎉 YOLOv5算法(ver.6及ver.5)的Qt-GUI实现 🎉 Qt-GUI implementation of the YOLOv5 algorithm (ver.6 and ver.5). 基于YOLOv5的v5版本和v6版本及Javacr大佬的UI逻辑进行编写

EricFang 12 Dec 28, 2022
Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Paper | Blog OFA is a unified multimodal pretrained model that unifies modalities (i.e., cross-modality, vision, language) and tasks (e.g., image gene

OFA Sys 1.4k Jan 08, 2023
Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

16 Nov 19, 2022
Code release for Convolutional Two-Stream Network Fusion for Video Action Recognition

Convolutional Two-Stream Network Fusion for Video Action Recognition

Christoph Feichtenhofer 676 Dec 31, 2022
SNE-RoadSeg in PyTorch, ECCV 2020

SNE-RoadSeg Introduction This is the official PyTorch implementation of SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentati

242 Dec 20, 2022
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation By Qing Xu, Wenting Duan and Na He Requirements pytorch==1.1

Qing Xu 20 Dec 09, 2022
Keep CALM and Improve Visual Feature Attribution

Keep CALM and Improve Visual Feature Attribution Jae Myung Kim1*, Junsuk Choe1*, Zeynep Akata2, Seong Joon Oh1† * Equal contribution † Corresponding a

NAVER AI 90 Dec 07, 2022
Pytorch implementation of the paper: "SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive Image Deraining"

SAPNet This repository contains the official Pytorch implementation of the paper: "SAPNet: Segmentation-Aware Progressive Network for Perceptual Contr

11 Oct 17, 2022
scAR (single-cell Ambient Remover) is a package for data denoising in single-cell omics.

scAR scAR (single cell Ambient Remover) is a package for denoising multiple single cell omics data. It can be used for multiple tasks, such as, sgRNA

19 Nov 28, 2022
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language mod

20.5k Jan 08, 2023
Image De-raining Using a Conditional Generative Adversarial Network

Image De-raining Using a Conditional Generative Adversarial Network [Paper Link] [Project Page] He Zhang, Vishwanath Sindagi, Vishal M. Patel In this

He Zhang 216 Dec 18, 2022
Simple Baselines for Human Pose Estimation and Tracking

Simple Baselines for Human Pose Estimation and Tracking News Our new work High-Resolution Representations for Labeling Pixels and Regions is available

Microsoft 2.7k Jan 05, 2023
pytorch implementation of dftd2 & dftd3

torch-dftd pytorch implementation of dftd2 [1] & dftd3 [2, 3] Install # Install from pypi pip install torch-dftd # Install from source (for developer

33 Nov 28, 2022
Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy

Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy Simplex Algorithm is a popular algorithm for linear programmi

Reda BELHAJ 2 Oct 12, 2022