Narrow Results

In some practical situations – e.g., when treating a new illness – we do not have enough data to make valid statistical conclusions. In such situations, it is necessary to use expert knowledge – and thus, it is beneficial to use fuzzy techniques that were specifically designed to process such knowledge. At first glance, it may seem that in situations when we have large amounts of data, the relative importance of expert knowledge should decrease. However, somewhat surprisingly, it turns out that expert knowledge is still very useful in the current age of big data. In this paper, we explain how exactly (and why) expert knowledge is useful, and we overview efficient methods for processing this knowledge. This overview is illustrated by examples from environmental science, geosciences, engineering (in particular, aircraft maintenance and underwater robots), and medicine.

This paper, proposes the Multi-Criteria Decision Making (MCDM) methodology for selection of a maintenance strategy to assure the consistency and effectiveness of maintenance decisions. The methodology is based on an AHP-enhanced TOPSIS, VIKOR and benefit-cost ratio, in which the importance of the effectiveness appraisal criteria of a maintenance strategy is determined by the use of AHP. Furthermore, in the proposed methodology the different maintenance policies are ranked using the benefit-cost ratio, TOPSIS and VIKOR. The method provides a basis for consideration of different priority factors governing decisions, which may include the rate of return, total profit, or lowest investment. When the preference is the rate of return, the benefit-cost ratio is used, and for the total profit TOPSIS is applied. In cases where the decision maker has specific preferences, such as the lowest investment, VIKOR is adopted. The proposed method has been tested through a case study within the aviation context for an aircraft system. It has been found that using the methodology presented in the paper, the relative advantage and disadvantage of each maintenance strategy can be identified in consideration of different aspects, which contributes to the consistent and rationalized justification of the maintenance task selection. The study shows that application of the combined AHP, TOPSIS, and VIKOR methodologies is an applicable and effective way to implement a rigorous approach for identifying the most effective maintenance alternative.

This paper envisions the possibility of a Conscious Aircraft: an aircraft of the future with features of consciousness. To serve this purpose, three main fields are examined: philosophy, cognitive neuroscience, and Artificial Intelligence (AI). While philosophy deals with the concept of what is consciousness, cognitive neuroscience studies the relationship of the brain with consciousness, contributing toward the biomimicry of consciousness in an aircraft. The field of AI leads into machine consciousness. The paper discusses several theories from these fields and derives outcomes suitable for the development of a Conscious Aircraft, some of which include the capability of developing “world-models”, learning about self and others, and the prerequisites of autonomy, selfhood, and emotions. Taking these cues, the paper focuses on the latest developments and the standards guiding the field of autonomous systems, and suggests that the future of autonomous systems depends on its transition toward consciousness. Finally, inspired by the theories suggesting the levels of consciousness, guided by the Theory of Mind, and building upon state-of-the-art aircraft with autonomous systems, this paper suggests the development of a Conscious Aircraft in three stages: Conscious Aircraft with (1) System-awareness, (2) Self-awareness, and (3) Fleet-awareness, from the perspectives of health management, maintenance, and sustainment.