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Samuel F. Kovacic

Researcher at Old Dominion University

Publications -  16
Citations -  166

Samuel F. Kovacic is an academic researcher from Old Dominion University. The author has contributed to research in topics: Computer science & System of systems. The author has an hindex of 5, co-authored 12 publications receiving 136 citations.

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Journal ArticleDOI

System of systems engineering: an emerging multidiscipline

TL;DR: An assessment of the current state of SOSE field development is provided, the nature of complex issues for which traditional approaches are falling short are suggested to include the corresponding challenges facing SOSE development, and an emerging paradigm for SOSE as a multidiscipline based on current research is established.
Proceedings ArticleDOI

Complex situations: An alternative approach for viewing a System of Systems

TL;DR: The paper provides an alternative approach for viewing wicked problems and concludes that system of systems as a wicked problem require a paradigm that is able to deal with this perspective.
Journal ArticleDOI

Research Agenda for Complex Situations

TL;DR: In this article, a research agenda describes the scientist's search for knowledge based on Epistemological, Ontological and Methodological facets, and identifies what is to be researched, how it to be studied, and what is sought from this research.
BookDOI

Managing and Engineering in Complex Situations

TL;DR: Sousa-Poza and Kovacic as mentioned in this paper described the Wicked Problem of a Cognitive Environment in Complex Situations and proposed the Reverse Decision Making (RDM) process for pragmatic decision making.
Proceedings ArticleDOI

Leveraging Transfer Learning and GAN Models for OCR from Engineering Documents

TL;DR: This paper explores deep learning models and OCR methods to effectively extract textual information from engineering documents collected by the NAVY's military sealift command division using a deep learning-based optical character recognition (OCR) framework, which integrates several modules including a pre-trained text detection model, a fine-tuned OCR algorithm, and a deep generative model to augment data for the fine- Tuning.