600 research outputs found
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Exploring adaptation & self-adaptation in autonomic computing systems
This panel paper sets out to discuss what self-adaptation
means, and to explore the extent to which current
autonomic systems exhibit truly self-adaptive behaviour.
Many of the currently cited examples are clearly
adaptive, but debate remains as to what extent they are
simply following prescribed adaptation rules within preset
bounds, and to what extent they have the ability to
truly learn new behaviour. Is there a standard test that
can be applied to differentiate? Is adaptive behaviour
sufficient anyway? Other autonomic computing issues are
also discussed
Autonomic computing architecture for SCADA cyber security
Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator
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A survey on online monitoring approaches of computer-based systems
This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
Industry 4.0 Technological Advancement in the Food and Beverage Manufacturing Industry in South Africa—Bibliometric Analysis via Natural Language Processing
The food and beverage (FOODBEV) manufacturing industry is a significant contributor to global economic development, but it is also subject to major global competition. Manufacturing technology evolution is rapid and, with the Fourth Industrial Revolution (4IR), ever accelerating. Thus, the ability of companies to review and identify appropriate, beneficial technologies and forecast the skills required is a challenge. 4IR technologies, as a collection of tools to assist technological advancement in the manufacturing sector, are essential. The vast and diverse global technology knowledge base, together with the complexities associated with screening in technologies and the lack of appropriate enablement skills, makes technology selection and implementation a challenge. This challenge is premised on the knowledge that there are vast amounts of information available on various research databases and web search engines; however, the extraction of specific and relevant information is time-intensive. Whilst existing techniques such as conventional bibliometric analysis are available, there is a need for dynamic approaches that optimise the ability to acquire the relevant information or knowledge within a short period with minimum effort. This research study adopts smart knowledge management together with artificial intelligence (AI) for knowledge extraction, classification, and adoption. This research defines 18 FOODBEV manufacturing processes and adopts a two-tier Natural Language Processing (NLP) protocol to identify technological substitution for process optimisation and the associated skills required in the FOODBEV manufacturing sector in South Africa
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