Hagenberg CampusResearch Center

Software Technology and Applications Research Center Hagenberg Campus

Publications

Supporting Teamwork in Industrial Virtual Reality Applications

J. Wolfartsberger, J. Zenisek, N. Wild - Supporting Teamwork in Industrial Virtual Reality Applications - Procedia Manufacturing, 2020, pp. 2-7
Virtual Reality (VR) systems allow for novel modes of visualization and interaction to support engineering design reviews. However, there are still research…   mehr
  • Journal
  • Software Technology and Applications
  • Materials and Production Engineering

Copyright relevance of AI-generated and encrypted Content

P. Burgstaller, E. Hermann - Copyright relevance of AI-generated and encrypted Content - Journal for IP and Copyright, 2020
The use of artificially intelligent (AI) systems affects almost all areas of law - from employment law and data protection law to health and care to "classic"…   mehr
  • Journal
  • Software Technology and Applications

Pre- and Post-processing Strategies for Generic Slice-wise Segmentation of Tomographic 3D datasets Utilizing U-Net Deep Learning Models Trained for Specific Diagnostic Domains

G. Zwettler, D. Holmes III, W. Backfrieder - Pre- and Post-processing Strategies for Generic Slice-wise Segmentation of Tomographic 3D datasets Utilizing U-Net Deep Learning Models Trained for Specific Diagnostic Domains - Proceedings of the VISAPP 2020, Valetta, Malta, 2020, pp. 66-78
  • Proceedings
  • Software Technology and Applications

MS Annika: A New Search Tool for the Identification of Cross-Linked Peptides from Tandem Mass Spectrometry Data

G. J. Pirklbauer, C. Stieger, S. M. Winkler, K. Mechtler, V. Dorfer - MS Annika: A New Search Tool for the Identification of Cross-Linked Peptides from Tandem Mass Spectrometry Data - Proceedings of the 9th Symposium on Structural Proteomics (SSP2019), Göttingen, Germany, 2019
Numerous chemical cross-linkers linkers have been developed over the last years, each with their own physical and chemical properties [1]. The development of…   mehr
  • Proceedings
  • Software Technology and Applications
  • Food Technology and Biotechnology

Parameter identification for symbolic regression using nonlinear least squares

M. Kommenda, B. Burlacu, G. K. Kronberger, M. Affenzeller - Parameter identification for symbolic regression using nonlinear least squares - Genetic Programming and Evolvable Machines, 2019, pp. 471-501
In this paper we analyze the effects of using nonlinear least squares for parameter identification of symbolic regression models and integrate it as local…   mehr
  • Journal
  • Software Technology and Applications

Using Ontologies to Express Prior Knowledge for Genetic Programming

S. Prieschl, D. Girardi, G. K. Kronberger - Using Ontologies to Express Prior Knowledge for Genetic Programming - Machine Learning and Knowledge Extraction. CD-MAKE 2019., Canterbury, United Kingdom of Great Britain and Northern Ireland, 2019, pp. 362-376
Ontologies are useful for modeling domains and can be used to capture expert knowledge about a system. Genetic programming can be used to identify statistical…   mehr
  • Proceedings
  • Software Technology and Applications

A Cost Optimized Data Acquisition System For Predictive Maintenance

E. Strumpf, F. Holzinger, F. Eibensteiner, J. Langer - A Cost Optimized Data Acquisition System For Predictive Maintenance - 6. Tagung Innovation Messtechnik, Linz, Austria, 2019, pp. 104-108
In this paper, we focus on the development and application of a cost optimized sensor platform for predictive maintenance of industrial fans. Typically,…   mehr
  • Proceedings
  • Software Technology and Applications

Preprocessing and Modeling of Radial Fan Data for Health State Prediction

F. Holzinger, M. Kommenda - Preprocessing and Modeling of Radial Fan Data for Health State Prediction - Computer Aided Systems Theory – EUROCAST 2019, Las Palmas, Gran Canaria, Spain, 2019
Monitoring critical components of systems is a crucial step towards failure safety. Affordable sensors are available and the industry is in the process of…   mehr
  • Proceedings
  • Software Technology and Applications

Extended Regression Models for Predicting the Pumping Capability and Viscous Dissipation of Two-Dimensional Flows in Single-Screw Extrusion

W. Roland, M. Kommenda, C. Marschik, J. Miethlinger - Extended Regression Models for Predicting the Pumping Capability and Viscous Dissipation of Two-Dimensional Flows in Single-Screw Extrusion - Polymers, Vol. 11, No. 2, 2019
Generally, numerical methods are required to model the non-Newtonian flow of polymer melts in single-screw extruders. Existing approximation equations for…   mehr
  • Journal
  • Software Technology and Applications

New Approaches for Equalizing the Granulate Size and Bulk Density in Mechanical Recycling Using Heuristic Approaches Based on Specific Data Analyses

M. Aigner, L. Kammerer, F. Schieder, G. K. Kronberger - New Approaches for Equalizing the Granulate Size and Bulk Density in Mechanical Recycling Using Heuristic Approaches Based on Specific Data Analyses - Proceedings of SPE ANTEC 2019, Detroit, United States of America, 2019, pp. 1-7
Exactly defined and constant granulates become more and more important in recycling business. The material is very often mixed with virgin granulate, sold on…   mehr
  • Proceedings
  • Software Technology and Applications

Analyzing the potential of Virtual Reality for engineering design review

J. Wolfartsberger - Analyzing the potential of Virtual Reality for engineering design review - Automation in Construction, 2019, pp. 27-37
Virtual Reality (VR) technology still needs to evolve, but as the pace of innovations accelerates, systems allow for more novel modes of visualization and…   mehr
  • Journal
  • Production and Operations Management
  • Software Technology and Applications

Integration of Physical Knowledge in Empirical Models - A New Approach to Regression Analysis

G. K. Kronberger, S. Scheidel, C. Haider, M. Kommenda, M. Kordon - Integration of Physical Knowledge in Empirical Models - A New Approach to Regression Analysis - 8th International Symposium on Development Methodology, Wiesbaden, Germany, 2019, pp. 1-9
Design of experiments, empirical modelling and model-based optimization is a widely known and approved approach for high-dimensional optimization problems in…   mehr
  • Proceedings
  • Software Technology and Applications

Extracting High-Level System Specifications from Source Code via Abstract State Machines

F. Ferrarotti, J. Pichler, M. Moser, G. Buchgeher - Extracting High-Level System Specifications from Source Code via Abstract State Machines - International Conference on Model and Data Engineering, Tuolouse, France, France, 2019, pp. 267-283
  • Proceedings
  • Software Technology and Applications

Understanding and Preparing Data of Industrial Processes for Machine Learning Applications

P. Fleck, M. Kügel, M. Kommenda - Understanding and Preparing Data of Industrial Processes for Machine Learning Applications - Computer Aided Systems Theory – EUROCAST 2019, Las Palmas, Gran Canaria, Spain, 2019
Industrial applications of machine learning face unique challenges due to the nature of raw industry data. Preprocessing and preparing raw industrial data…   mehr
  • Proceedings
  • Software Technology and Applications

Lost in Translation: Machine Translation and Text-To-Speech in Industry 4.0

J. D. Hallewell Haslwanter, M. Heiml, J. Wolfartsberger - Lost in Translation: Machine Translation and Text-To-Speech in Industry 4.0 - Proceedings of the 12th PErvasive Technologies Related to Assistive Environments Conference, Rhodos, Greece, 2019, pp. 10
Small lot sizes are becoming more common in modern manufacturing. Rather than automate every possible product variant, companies may rely on manual assembly to…   mehr
  • Proceedings
  • Production and Operations Management
  • Software Technology and Applications
  • Automated Control Engineering and Simulation

Management of Predictive Models in Industrial Applications

F. Bachinger, G. K. Kronberger - Management of Predictive Models in Industrial Applications - Coming Soon - Proceedings - 2019, Linz, Austria, 2019, pp. 253-259
  • Proceedings
  • Software Technology and Applications

Local Optimization and Complexity Control for Symbolic Regression

M. Kommenda - Local Optimization and Complexity Control for Symbolic Regression - Phd Thesis, Johannes Kepler Universität, Austria, 2018, pp. 1-157
Symbolic regression is a data-based machine learning approach that creates interpretable prediction models in the form of mathematical expressions without the…   mehr
  • Report
  • Software Technology and Applications

Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems

G. K. Kronberger, M. Kommenda, E. Lughofer, S. Saminger-Platz, A. Promberger, F. Nickel, S. M. Winkler, M. Affenzeller - Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems - Applied Soft Computing, 2018, pp. 610-624
Tribological systems are mechanical systems that rely on friction to transmit forces. The design and dimensioning of such systems requires prediction of…   mehr
  • Journal
  • Software Technology and Applications

Schema Analysis in Tree-based Genetic Programming

B. Burlacu, M. Affenzeller, M. Kommenda, G. K. Kronberger, S. M. Winkler - Schema Analysis in Tree-based Genetic Programming in Genetic Programming in Theory and Practice XV (Contributions to Book: Part/Chapter/Section 2), - Springer International Publishing, 2018, pp. 17-37
In this chapter we adopt the concept of schemata from schema theory and use it to analyze population dynamics in genetic programming for symbolic regression.…   mehr
  • Book
  • Software Technology and Applications

Schema-based Diversification in Genetic Programming

B. Burlacu, M. Affenzeller - Schema-based Diversification in Genetic Programming - GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, Japan, 2018, pp. 1111-1118
In genetic programming (GP), population diversity represents a key aspect of evolutionary search and a major factor in algorithm performance. In this paper we…   mehr
  • Proceedings
  • Software Technology and Applications

Novel Robustness Measures for Engineering Design Optimisation

P. Fleck, M. Kommenda, T. Prante, M. Affenzeller - Novel Robustness Measures for Engineering Design Optimisation - International Journal of Simulation and Process Modelling, Vol. 13, No. 4, 2018
This paper presents novel robustness measures to analyse and compare the robustness of solutions for constrained optimisation problems in the field of…   mehr
  • Journal
  • Software Technology and Applications

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