DSpace Collection:http://dspace.wunu.edu.ua/handle/316497/149102024-03-29T06:39:55Z2024-03-29T06:39:55ZUse of Artificial Intelligence Techniques for Prognostics: New Application of Rough SetsZalewski, JanuszWojcik, Zbigniewhttp://dspace.wunu.edu.ua/handle/316497/332422019-03-30T13:03:14Z2012-01-01T00:00:00ZTitle: Use of Artificial Intelligence Techniques for Prognostics: New Application of Rough Sets
Authors: Zalewski, Janusz; Wojcik, Zbigniew
Abstract: The objective of this paper is to set the context for the potential application of rough sets in prognostics.
Prognostics is a field of engineering, which deals with predicting faults and failures in technical systems. Engineering
solutions to respective problems embrace the use of multiple Artificial Intelligence (AI) techniques. The authors, first,
review selected AI techniques used in prognostics and then propose the application of rough sets to build the system
health prognostication model.2012-01-01T00:00:00ZThe Art and Science of GPU and Multi-Core ProgrammingHiromoto, Robert E.http://dspace.wunu.edu.ua/handle/316497/332412019-03-30T13:19:51Z2012-01-01T00:00:00ZTitle: The Art and Science of GPU and Multi-Core Programming
Authors: Hiromoto, Robert E.
Abstract: This paper examines the computational programming issues that arise from the introduction of GPUs and
multi-core computer systems. The discussions and analyses examine the implication of two principles (spatial and
temporal locality) that provide useful metrics to guide programmers in designing and implementing efficient sequential
and parallel application programs. Spatial and temporal locality represents a science of information flow and is
relevant in the development of highly efficient computational programs. The art of high performance programming is to
take combinations of these principles and unravel the bottlenecks and latencies associate with the architecture for each
manufacturer computer system, and develop appropriate coding and/or task scheduling schemes to mitigate or
eliminate these latencies.2012-01-01T00:00:00ZAlgebraic Approach to Information Fusion in Ontology-Based Modeling SystemsArtemieva, IrinaZuenko, AlexanderFridman, Аlexanderhttp://dspace.wunu.edu.ua/handle/316497/332402019-03-30T13:20:31Z2012-01-01T00:00:00ZTitle: Algebraic Approach to Information Fusion in Ontology-Based Modeling Systems
Authors: Artemieva, Irina; Zuenko, Alexander; Fridman, Аlexander
Abstract: In this paper we discuss the possibilities to use algebraic methods (in particular, n-tuple algebra developed
by the authors) to improve the functioning of convenient ontology-based modeling systems. An illustrative example
shows the ways to unify representation and processing of two major parts of subject domain ontologies.2012-01-01T00:00:00ZMulti-Agent Parallel Implementation of Photomask Simulation in PhotolithographyAvakaw, Syarhei M.Doudkin, Alexander A.Inyutin, Alexander V.Otwagin, Aleksey V.Rusetsky, Vladislav A.http://dspace.wunu.edu.ua/handle/316497/332392019-03-30T13:21:32Z2012-01-01T00:00:00ZTitle: Multi-Agent Parallel Implementation of Photomask Simulation in Photolithography
Authors: Avakaw, Syarhei M.; Doudkin, Alexander A.; Inyutin, Alexander V.; Otwagin, Aleksey V.; Rusetsky, Vladislav A.
Abstract: A framework for paralleling aerial image simulation in photolithography is proposed. Initial data for the
simulation representing photomask are considered as a data stream that is processed by a multi-agent computing
system. A parallel image processing is based on a graph model of a parallel algorithm. The algorithm is constructed
from individual computing operations in a special visual editor. Then the visual representation is converted into XML,
which is interpreted by the multi-agent system based on MPI. The system performs run-time dynamic optimization of
calculations using an algorithm of virtual associative network. The proposed framework gives a possibility to design
and analyze parallel algorithms and to adapt them to architecture of the computing cluster.2012-01-01T00:00:00Z