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This paper presents an expert system for selecting a proper IT infrastructure for smart grid, based on a novel fuzzy approach to Tricotyledon Theory of System Design (Fuzzy-T3SD). The proposed Fuzzy-T3SD is a policy-driven decision making method that can change the result of the decision makings according to the utility's policy. The Fuzzy-T3SD is applied to the practical data of the Greater Tehran...
Interaction of human and computer agents should be harmonized by adapting the automation level of the IT systems to maintain a high performance for the system in changing environmental conditions. This research presents an expert system for the realization of adaptive autonomy (AA), using Petri Net (PN), referred to as AAPNES, based on practical list of environmental conditions and superior experts'...
Smart grid expectations objectify the need for optimizing power distribution systems greater than ever. Distribution Automation (DA) is an integral part of the SG solution; however, disregarding human factors in the DA systems can make it more problematic than beneficial. As a consequence, Human-Automation Interaction (HAI) theories can be employed to optimize the DA systems in a human-centered manner...
We have introduced a novel framework for realization of Adaptive Autonomy (AA) in human-automation interaction (HAI) systems, as well as several expert system realizations of that. This study presents an expert system for realization of AA, using logistic regression (LR), referred to as Adaptive Autonomy Logistic Regression Expert System (AALRES). The proposed system prescribes proper Levels of Automation...
Intelligent control and automation is associated with expert systems; especially, when it needs to human expertise. Earlier we introduced a framework for implementation of adaptive autonomy (AA) in human-automation interaction systems, followed by a data-fusion-equipped expert system to realize that. This paper uses fuzzy sets concept to realize the AA expert system, in a real automation application...
In this paper a new hybrid method for training fuzzy cognitive maps is presented. FCMs are based on the knowledge of human experts and may not be accurate enough because of probable mistakes of experts. Thus, some learning methods have been investigated to train FCMs, so that these probable mistakes are covered. Two learning methods, PSO and NHL, and a new hybrid of them are introduced and implemented...
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