scumechanics Goto Github PK

odml-pathways

onnxed-wseunet

Wide residual Unet for generate or segmentation

optimizeddeepencoderdecodercracksegmentation

Optimized deep encoder-decoder methods for crack segmentation

pab

This project aims to promote Organic farming by reducing usage of weedicide and pesticide which results in soil degradation. · With help of proper Image segmentation and Machine Learning technique i.e. CNN, we are able to distinguish Ladyfinger plant with weeds on field. · We implemented YOLO algorithm to make real time Object detection possible. · We have 4 wheel drive for PAB which enables it to move in field easily we used K mean clustering algorithm for lane detection and autonomous drive.

paperweekly

Papers for CNN, object detection, keypoint detection, semantic segmentation, medical image processing, SLAM, etc.

pcb_defect_inspection_by_unet

pclsegmentation

Tensorflow 2.4 Pipeline for Semantic Pointcloud Segmentation with SqueezeSeqV2, Darknet21 and Darknet52.

phosa

Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild

pifuhd

High-Resolution 3D Human Digitization from A Single Image.

pinn-elastodynamics

physics-informed neural network for elastodynamics problem

pix2pix_cfd

An AI model to depict fluid flow.

pixellib

pothole-dataset

my dataset

pre-trained-deep-learning-models-for-rapid-analysis-of-piezoelectric-hysteresis-loops-sho-fitting

Temporary repository for research paper about Accelerated Fitting of BEPS

predicting_concrete_compressive_strength

How would you predict the compressive strength of concrete as a function of its constituent materials and curing time? In this portfolio project, I optimize a model for determining concrete compressive strength using a deep neural network in Tensorflow 2.0 and compare its performance to linear models.

protein-ebm

Energy-based models for atomic-resolution protein conformations

pyaez

PyAEZ is a python package consisted of many algorithms related to Agro-ecalogical zoning (AEZ) framework.

pylabfea

Python Laboratory for Finite Element Analysis

python-for-geospatial-data-analysis

This includes short and minimalistic few sessions covering fundamentals of Python programing language for geospatial data analysis.

python_for_microscopists

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

pytorch3d

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

pytorch_concrete_inspection

The Pytorch Implementation of Our Concrete Crack and Spall Detection

rainfall_predictor

Use rainfall data predict landslide occur or not

rbf-topology-optimization

research-on-solving-partial-differential-equations-of-solid-mechanics-based-on-pinn

This is the code of my master thesis.

revealing-ferroelectric-switching-character-using-deep-recurrent-neural-networks

The ability to manipulate domains and domain walls underpins function in a range of next-generation applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of features of nanoscale ferroelectric switching from multichannel hyperspectral band-excitation piezoresponse force microscopy of tensile-strained PbZr0.2Ti0.8O3 with a hierarchical domain structure. Using this approach, we identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we are able to identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of the physical response of a material from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging multimodal in operando spectroscopies and automated control for the manipulation of nanoscale structures in materials.

rf-vda-landslide-map

Modello Random Forest per la creazione di una mappa di suscettibilità da frane superficiali // // Tesi di Laurea Magistrale in Scienze della Terra (Geologia Applicata) - Università degli Studi di Milano

roaddamagedetector

saliency-maps-of-cracked-concrete

The Saliency map shows exactly which pixels are most used to make to prediction ,can be seen as rough image segementation

sar_denoising

Denoising framework for SAR images (Synthetic Aperture Radar) based on the FFDNet. Final project of MVA course "Remote sensing data: from sensor to large-scale geospatial data exploitation"