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

deep learning model with only python and numpy with test accuracy 99 % on mnist dataset and different optimization choices

Face-Recognition-Attendence-System - This face recognition Attendence system using Python

Official implementation of "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision" ECCV2020

GeneDisco is a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery.

VGGVox models for Speaker Identification and Verification trained on the VoxCeleb (1 & 2) datasets

Training Very Deep Neural Networks Without Skip-Connections

PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models

Exploration & Research into cross-domain MEV. Initial focus on ETH/POLYGON.

Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test.

Goal of the project : Detecting Temporal Boundaries in Sign Language videos

COD-Rank-Localize-and-Segment (CVPR2021)

Open source code for the paper of Neural Sparse Voxel Fields.

Deep Reinforcement Learning for Multiplayer Online Battle Arena

Code for CVPR2021 paper "Learning Salient Boundary Feature for Anchor-free Temporal Action Localization"

Rest API Written In Python To Classify NSFW Images.

GRF: Learning a General Radiance Field for 3D Representation and Rendering

A simple code to convert image format and channel as well as resizing and renaming multiple images.

TrackFormer: Multi-Object Tracking with Transformers

KwaiRec: A Fully-observed Dataset for Recommender Systems (Density: Almost 100%)

Simple improvement of VQVAE that allow to generate x2 sized images compared to baseline