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A nearly autonomous management and control (NAMAC) system is designed to furnish recommendations to operators for achieving particular goals based on NAMACs knowledge base. As a critical component in a NAMAC system, digital twins (DTs) are used to extract information from the knowledge base to support decision-making in reactor control and management during all modes of plant operations. With the advancement of artificial intelligence and data-driven methods, machine learning algorithms are used to build DTs of various functions in the NAMAC system. To evaluate the uncertainty of DTs and its impacts on the reactor digital instrumentation and control systems, uncertainty quantification (UQ) and software risk analysis is needed. As a comprehensive overview of prior research and a starting point for new investigations, this study selects and reviews relevant UQ techniques and software hazard and software risk analysis methods that may be suitable for DTs in the NAMAC system.
Industry in all sectors is experiencing a profound digital transformation that puts software at the core of their businesses. In order to react to continuously changing user requirements and dynamic markets, companies need to build robust workflows t
This paper develops a Nearly Autonomous Management and Control (NAMAC) system for advanced reactors. The development process of NAMAC is characterized by a three layer-layer architecture: knowledge base, the Digital Twin (DT) developmental layer, and
The Nearly Autonomous Management and Control System (NAMAC) is a comprehensive control system that assists plant operations by furnishing control recommendations to operators in a broad class of situations. This study refines a NAMAC system for makin
Software and IT industry in Pakistan have seen a dramatic growth and success in past few years and is expected to get doubled by 2020, according to a research. Software development life cycle comprises of multiple phases, activities and techniques th
Representative sampling appears rare in empirical software engineering research. Not all studies need representative samples, but a general lack of representative sampling undermines a scientific field. This article therefore reports a systematic rev