A data-driven maritime port simulator

Related tags

Deep Learningpyseidon
Overview

PySeidon - A Data-Driven Maritime Port Simulator 🌊

Image of the simulation software

Extendable and modular software for maritime port simulation.

This software uses entity-component system approach making it highly customizable for various end goals and easily built upon.

Overview

PySeidon was primarily designed for port scenario testing, but can be used for a variety of other tasks. Software can be adapted to simulate any maritime port provided that the required data is available. The simulator can be tested with different factors, such as:

  • New/different anchorage location
  • Different number of tugboat/pilots available
  • Different priority order depending on ship class/size
  • Etc...

PySeidon's output can then give useful insights whether the given change improves certain Key Performance Indicators (check this repository for scripts to analyse simulation results).

PySeidon can be used to create new data for various downstream tasks (e.g. anomaly detection), approximate impact on Key Performance Indicators of some decision, novelty introduced in a port. The supplemental visualization software can be used to analyse general (or created by simulation) AIS data over time or analyse simulation states (for debugging).

Installation and Demo

The framework is bundled with an example model to get you started. To run it first install the dependencies by running pip install -r requirements.txt. Pip might complain about libgeos not being installed on your system. On Ubuntu you can install it by running sudo apt-get install libgeos-dev.

Once the required libraries are installed run the example model with the following command (it may take a bit for the first vessel to spawn)

python main.py          \
    --out sim-output    \
    --step 10           \
    --verbose y         \
    --graphics y        \
    --cache y           \
    --seed 567

Features

  • Simulation of the following agents and infrastructure elements
    • Agents: vessel, tugboats, pilots
    • Infrastructure components: berths, anchorages, tugboat rendezvous and storage locations, pilot rendezvous and storage locations
    • Introduction of anomalies such as randomized berth inspections, tugboat malfunctions, anomalous vessel velocity. These can be used to create datasets that are currently not available
  • Visualization of the simulation: infrastructure components and agents, including an overview of vessel and berth information at any moment in time
  • Simulation of anomalies: random berth inspection, tugboat malfunctions, unusual vessel velocities
  • Clean way of conducting experiments of the simulation (multiple runs, no graphics, aggregating output data of the simulation)
  • The simulation engine relies on the input data, minimal actual code modification (model and main.py) is required to adapt to different maritime ports if no additional features are to be implemented

Documentation

For detailed instructions how to install and use PySeidon, see the Documentation.

Future work

  • Various external factors such as weather, tide, etc.
  • Implement proper nautical rules
  • Loading simulation from a saved state
  • GUI to enable non-experts be able to use the software
  • Boatmen agent
  • Better vessel acceleration model, PID controller
  • Automatic data analysis at the end of simulation
[NeurIPS 2021] Source code for the paper "Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes"

Qu-ANTI-zation This repository contains the code for reproducing the results of our paper: Qu-ANTI-zation: Exploiting Quantization Artifacts for Achie

Secure AI Systems Lab 8 Mar 26, 2022
quantize aware training package for NCNN on pytorch

ncnnqat ncnnqat is a quantize aware training package for NCNN on pytorch. Table of Contents ncnnqat Table of Contents Installation Usage Code Examples

62 Nov 23, 2022
MINOS: Multimodal Indoor Simulator

MINOS Simulator MINOS is a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environ

194 Dec 27, 2022
Pytorch implementation of PTNet for high-resolution and longitudinal infant MRI synthesis

Pyramid Transformer Net (PTNet) Project | Paper Pytorch implementation of PTNet for high-resolution and longitudinal infant MRI synthesis. PTNet: A Hi

Xuzhe Johnny Zhang 6 Jun 08, 2022
FedGS: A Federated Group Synchronization Framework Implemented by LEAF-MX.

FedGS: Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT Preparation For instructions on generating data, plea

Lizonghang 9 Dec 22, 2022
Human segmentation models, training/inference code, and trained weights, implemented in PyTorch

Human-Segmentation-PyTorch Human segmentation models, training/inference code, and trained weights, implemented in PyTorch. Supported networks UNet: b

Thuy Ng 474 Dec 19, 2022
A faster pytorch implementation of faster r-cnn

A Faster Pytorch Implementation of Faster R-CNN Write at the beginning [05/29/2020] This repo was initaited about two years ago, developed as the firs

Jianwei Yang 7.1k Jan 01, 2023
Optimizing Value-at-Risk and Conditional Value-at-Risk of Black Box Functions with Lacing Values (LV)

BayesOpt-LV Optimizing Value-at-Risk and Conditional Value-at-Risk of Black Box Functions with Lacing Values (LV) About This repository contains the s

1 Nov 11, 2021
Rethinking the U-Net architecture for multimodal biomedical image segmentation

MultiResUNet Rethinking the U-Net architecture for multimodal biomedical image segmentation This repository contains the original implementation of "M

Nabil Ibtehaz 308 Jan 05, 2023
A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.

Layer-wise Relevance Propagation (LRP) in PyTorch Basic unsupervised implementation of Layer-wise Relevance Propagation (Bach et al., Montavon et al.)

Kai Fabi 28 Dec 26, 2022
TransReID: Transformer-based Object Re-Identification

TransReID: Transformer-based Object Re-Identification [arxiv] The official repository for TransReID: Transformer-based Object Re-Identification achiev

569 Dec 30, 2022
A flexible submap-based framework towards spatio-temporally consistent volumetric mapping and scene understanding.

Panoptic Mapping This package contains panoptic_mapping, a general framework for semantic volumetric mapping. We provide, among other, a submap-based

ETHZ ASL 194 Dec 20, 2022
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models (ICCV 2021 Oral)

ILVR + ADM This is the implementation of ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models (ICCV 2021 Oral). This repository is h

Jooyoung Choi 225 Dec 28, 2022
A python/pytorch utility library

A python/pytorch utility library

Jiaqi Gu 5 Dec 02, 2022
Keras Image Embeddings using Contrastive Loss

Image to Embedding projection in vector space. Implementation in keras and tensorflow of batch all triplet loss for one-shot/few-shot learning.

Shravan Anand K 5 Mar 21, 2022
Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning

Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning Yansong Tang *, Zhenyu Jiang *, Zhenda Xie *, Yue

Zhenyu Jiang 12 Nov 16, 2022
Implementation of "Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency"

Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency (ICCV2021) Paper Link: https://arxiv.org/abs/2107.11355 This implementation bui

32 Nov 17, 2022
Neural network for recognizing the gender of people in photos

Neural Network For Gender Recognition How to test it? Install requirements.txt file using pip install -r requirements.txt command Run nn.py using pyth

Valery Chapman 1 Sep 18, 2022
[CVPR 2022 Oral] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

EPro-PnP EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation In CVPR 2022 (Oral). [paper] Hanshen

同济大学智能汽车研究所综合感知研究组 ( Comprehensive Perception Research Group under Institute of Intelligent Vehicles, School of Automotive Studies, Tongji University) 842 Jan 04, 2023
Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗

urban_road_filter: a real-time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles Dependency ROS (tested with Kinetic and

JKK - Vehicle Industry Research Center 180 Dec 12, 2022