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Found 15 papers, 0 papers with code
Land Classification in Satellite Images by Injecting Traditional Features to CNN Models
In this study, we propose a novel method to improve the accuracy of CNN models, especially the ones with small size, by injecting traditional features to them.
Aim in Climate Change and City Pollution
The sustainability of urban environments is an increasingly relevant problem.
Interpretable deep-learning models to help achieve the Sustainable Development Goals
We discuss our insights into interpretable artificial-intelligence (AI) models, and how they are essential in the context of developing ethical AI systems, as well as data-driven solutions compliant with the Sustainable Development Goals (SDGs).
Recurrent U-net for automatic pelvic floor muscle segmentation on 3D ultrasound
In this study we present a U-net like neural network with some convolutional long short term memory (CLSTM) layers to automate the 3D segmentation of the levator ani muscle (LAM) in TPUS volumes.
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Inspection and maintenance are two crucial aspects of industrial pipeline plants.
Autonomous Navigation
Hierarchical Reinforcement Learning
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Remote sensing and AI for building climate adaptation applications
Urban areas are not only one of the biggest contributors to climate change, but also they are one of the most vulnerable areas with high populations who would together experience the negative impacts.
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics
Results show that the novel framework can efficiently learn low-dimensional and interpretable state and action representations and the optimal latent policy.
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life.
From coarse wall measurements to turbulent velocity fields through deep learning
The method is applied both for the resolution enhancement of wall fields and the estimation of wall-parallel velocity fields from coarse wall measurements of shear stress and pressure.
Fluid Dynamics
Low Dimensional State Representation Learning with Reward-shaped Priors
We propose a method that aims at learning a mapping from the observations into a lower-dimensional state space.
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach
We present a map-less path planning algorithm based on Deep Reinforcement Learning (DRL) for mobile robots navigating in unknown environment that only relies on 40-dimensional raw laser data and odometry information.
A low-cost real-time 3D imaging system for contactless asthma observation
However, it is very difficult to detect this disorder in them, since the breathing motion of children tends to change when they reach an age of 6.
3D tissue reconstruction with Kinect to evaluate neck lymphedema
Lymphedema is a condition of localized tissue swelling caused by a damaged lymphatic system.
Sequential image processing methods for improving semantic video segmentation algorithms
Herein we propose two sequential probabilistic video frame analysis approaches to improve the segmentation performance of the existing algorithms.
Semantic Segmentation of Skin Lesions using a Small Data Set
The central research question in this paper is "How to segment skin lesion images using a neural network with low available data?".
