Hands can: Determining the location and range of motion of digital joints in 3D scans

The versatility of the hands is revealed in its movements, but often not noticed before trauma occurs. Joint range of motion is used as a measure to follow the progress of diseases. A digital workflow for 3D data in medical appliances is envisioned for years.<br/>The aim of this research is to develop a method that reliably and reproducability...

Deep Learning Architectures for PM2.5 and Visibility Predictions

Facing the severe air pollution phenomenon in urban areas and the subsequent low visibility event in airports, it is urgent to conduct air quality and visibility predictions to better reflect their changing trends. However, the variations of PM2.5 and visibility involve complicated physical and chemical processes, which make their accurate...

Automated malaria diagnosis using convolutional neural networks in an on-field setting: The analysis of low quality smartphone based microscope images

This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone...

Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods

Recent research has seen several forecasting methods being applied for heat load forecasting of district heating networks. This paper presents two methods that gain significant improvements compared to the previous works. First, an automated way of handling non-linear dependencies in linear models is presented. In this context, the paper...

Short-term forecasting of solar irradiance without local telemetry: A generalized model using satellite data

Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g. operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain,...

Reinforcement learning for control: Performance, stability, and deep approximators

Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain. This review mainly covers artificial-intelligence approaches to RL, from the viewpoint of the control engineer. We explain how approximate representations of the...

Experience selection in deep reinforcement learning for control

Experience replay is a technique that allows off-policy reinforcement-learning methods to reuse past experiences. The stability and speed of convergence of reinforcement learning, as well as the eventual performance of the learned policy, are strongly dependent on the experiences being replayed. Which experiences are replayed depends on two...

Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and...