Systematic Comparison of Supervised Learning Methods to Reduce Calibration Effort in Engine Control Development

Complexity of engine control systems is continuously growing due to an increased number of subsystems and the need for robust performance. For traditional map-based as well as state-of-the-art model-based approaches, this will lead to unacceptable development costs and time for future engines. Parametrization of the embedded models using...

Supervised segmentation of NO2 plumes from individual ships using TROPOMI satellite data

The shipping industry is one of the strongest anthropogenic emitters of NOx—a substance harmful both to human health and the environment. The rapid growth of the industry causes societal pressure on controlling the emission levels produced by ships. All the methods currently used for ship emission monitoring are costly and require proximity to a...

A semi-supervised learning approach to study the energy consumption in smart buildings

In this work, we use the semi-supervised LAMDA-HSCC algorithm for characterizing the energy consumption in smart buildings, which can work with labeled and unlabeled data. Particularly, it uses the LAMDA-RD approach for the clustering problem and the LAMDA-HAD approach for the classification problem. Additionally, this algorithm uses three...

Supervised temporal link prediction in large-scale real-world networks

Link prediction is a well-studied technique for inferring the missing edges between two nodes in some static representation of a network. In modern day social networks, the timestamps associated with each link can be used to predict future links between so-far unconnected nodes. In these so-called temporal networks, we speak of temporal link...

Applying machine learning techniques for forecasting flexibility of virtual power plants

Proposed minimum reporting standards for data analysis in metabolomics

The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called meta-data). These definitions will embrace as many aspects of a complete...