Rod driven frequency entrainment and resonance phenomena. - In: Frontiers in human neuroscience, ISSN 1662-5161, Bd. 10 (2016), 413, insges. 12 S.
https://doi.org/10.3389/fnhum.2016.00413
Goal model integration for tailoring product line development processes. - In: International Journal of Advanced Computer Science and Applications, ISSN 2156-5570, Bd. 7 (2016), 7, S. 618-623
http://dx.doi.org/10.14569/IJACSA.2016.070784
Combination of confocal principle and aperture stop separation improves suppression of crystalline lens fluorescence in an eye model. - In: Biomedical optics express, ISSN 2156-7085, Bd. 7 (2016), 9, S. 3198-3210
http://dx.doi.org/10.1364/BOE.7.003198
Optimal magnetic sensor vests for cardiac source imaging. - In: Sensors, ISSN 1424-8220, Bd. 16 (2016), 6, 754, insges. 17 S.
http://dx.doi.org/10.3390/s16060754
On variable reverse power flow-part II: an electricity market model considering wind station size and location. - In: Energies, ISSN 1996-1073, Bd. 9 (2016), 4, 235, S. 1-13
http://dx.doi.org/10.3390/en9040235
Skull defects in finite element head models for source reconstruction from magnetoencephalography signals. - In: Frontiers in neuroscience, ISSN 1662-453X, Bd. 10 (2016), 141, S. 1-15
https://doi.org/10.3389/fnins.2016.00141
Solid-state dewetting of single- and bilayer Au-W thin films: unraveling the role of individual layer thickness, stacking sequence and oxidation on morphology evolution. - In: AIP Advances, ISSN 2158-3226, Bd. 6 (2016), 3, 035109, insges. 10 S.
http://dx.doi.org/10.1063/1.4944348
On variable reverse power flow-part I: active-reactive optimal power flow with reactive power of wind stations. - In: Energies, ISSN 1996-1073, Bd. 9 (2016), 3, 121, S. 1-12
http://dx.doi.org/10.3390/en9030121
Introducing a method for modeling knowledge bases in expert systems using the example of large software development projects. - In: International Journal of Advanced Computer Science and Applications, ISSN 2156-5570, Bd. 6.2015, 12, Paper 1, S. 1-7
Goal of this paper is to develop a meta-model, which provides the basis for developing highly scalable artificial intelligence systems that should be able to make autonomously decisions based on different dynamic and specific influences. An artificial neural network builds the entry point for developing a multi-layered human readable model that serves as knowledge base and can be used for further investigations in deductive and inductive reasoning. A graph-theoretical consideration gives a detailed view into the model structure. In addition to it the model is introduced using the example of large software development projects. The integration of Constraints and Deductive Reasoning Element Pruning are illustrated, which are required for executing deductive reasoning efficiently.
http://dx.doi.org/10.14569/IJACSA.2015.061201
A simple method for identifying parameter correlations in partially observed linear dynamic models. - In: BMC systems biology, ISSN 1752-0509, Bd. 9.2015, 92 (14. Dez.), insges. 14 S.
http://dx.doi.org/10.1186/s12918-015-0234-3