This paper presents the application of machine learning (ML) methods in setting up a model with the aim of predicting the energy efficiency of seagoing ships in the case of a vessel for the transport of liquefied petroleum gas (LPG). The ML algorithm is learned from shipboard automation system measurement data, noon logbook reports, and […]
The real-life signals captured by different measurement systems (such as modern maritime transport characterized by challenging and varying operating conditions) are often subject to various types of noise and other external factors in the data collection and transmission processes. Therefore, the filtering algorithms are required to reduce the noise level in measured signals, thus enabling […]
Prof. dr. sc. Todorka Glushkova from the Faculty of Mathematics and Informatics, Paisii Hilendarski Plovdiv University, Plovdiv, Bulgaria, held an interesting presentation on “Cyber-Physical Production Systems (CPPS)” on the 14th of April, 2021. The presentation also addressed her work in the field and research interests with potentials for further collaboration.
In this paper, we present an approach to the reconstruction of signals exhibiting sparsity in a transformation domain, having some heavily disturbed samples. This sparsity-driven signal recovery exploits a carefully suited random sampling consensus (RANSAC) methodology for the selection of a subset of inlier samples. To this aim, two fundamental properties are used: A signal […]
Prof. dr. sc. Sanda Martinčić-Ipšić and prof. dr. sc. Ana Meštrović held an interesting talks on 31st of March, 2021 presenting their work in the field of applications of artificial intelligence to natural language processing and social network analysis. Recording of the presentations:
29th Summer School on Image Processing – The annual gathering of researchers and professionals dealing with image analysis and machine vision. Rijeka, Croatia, 8-17 July 2021.
A news post has been published online about our latest published research regarding AI modelling of nanoscale friction.
An article about our research has been published on Tribonet.org portal concerning nanoscale friction modelling using AI.
An article about our research has been published on ScienceX portal.
Background and objectives: Computer-aided diagnosis relies on machine learning algorithms that require filtered and preprocessed data as the input. Aligning the image in the desired direction is an additional manual step in post- processing, commonly overlooked due to workload issues. Several state-of-the-art approaches for fracture detection and disease-struck region segmentation benefit from correctly oriented images, […]