Articolele autorului Viorel Ariton
Link la profilul stiintific al lui Viorel Ariton

Comparison and evaluation of ICT qualifications in Europe

ICT training is analysed and synthetically illustrated for the EUQuaSIT partner countries (Germany, Netherlands, Portugal, Czech Republic and Romania). Strength and weakness of education systems in partner countries are also presented, with recommendations on future European harmonization of the ICT qualification profiles (VET, HE and CVT levels).

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Deep and Shallow Knowledge in Fault Diagnosis

Diagnostic reasoning is fundamentally different from reasoning used in modelling or control: last is deductive (from causes to effects) while first is abductive (from effects to causes). Fault diagnosis in real complex systems is difficult due to multiple effects-to-causes relations and to various running contexts. In deterministic approaches deep knowledge is used to find explanations for effects in the target system (impractical when modelling

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Handling Qualitative Aspects of Human Knowledge in Diagnosis

Knowledge involved in diagnosis of real complex systems comes from human experts and requires appropriate discrete and qualitative representation. The large amount of information resulted is difficult to manage and prepare to enter the diagnosis system without the help of an appropriate tool. The paper proposes a knowledge elicitation scheme for multifunctional conductive flow systems faulty behaviour, along with appropriate representation of instance

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Human-like fault diagnosis using a neural network implementation of plausibility and relevance

In real systems, fault diagnosis is performed by a human diagnostician, and it encounters complex knowledge associations, both for normal and faulty behaviour of the target system. The human diagnostician relies on deep knowledge about the structure and the behaviour of the system, along with shallow knowledge on fault-to-manifestation patterns acquired from practice. This paper proposes a general approach to embed deep and shallow knowledge in neural

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