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Orest Storoshchuk

dblp:25/1408 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Requirements engineering and software design · 77% Program verification · 23%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Requirements engineering and software design › model-driven engineering
model-based design
0.012001
Modeling Manufacturing Control Software · ICRA 2001
Embedded and real-time systems
industrial control systems
0.012001
Modeling Manufacturing Control Software · ICRA 2001
Program verification
formal modeling
0.012001
Modeling Manufacturing Control Software · ICRA 2001

Methods — techniques the papers use, named apart from their topics

subsystem composition · 0.1finite state machine · 0.1
YearPublicationVenuePosition
2001 Modeling Manufacturing Control Software
abstract
Software is essential in today's machine controllers for reuse, reconfiguration and cost-reduction. Formal models and specifications for such a system are critical to guarantee correctness. In this paper, we present an approach to model manufacturing control systems based on a framework for subsystem composition and a nested finite state machine model for the system behavior. The paper presents the application of model-based methodology for real control systems. Some preliminary results obtained show that development costs were reduced significantly due to the domain-specific knowledge captured in such a model.
Orest Storoshchuk, Shige Wang, Kang G. Shin
ICRA1