Factors influencing aircraft ground handling performance

Problems associated with aircraft ground handling operations on wet runways are discussed and major factors which influence tire/runway braking and cornering traction capability are identified including runway characteristics, tire hydroplaning, brake system anomalies, and pilot inputs. Research results from tests with instrumented ground vehicles and aircraft, and aircraft wet runway accident investigation are summarized to indicate the effects of different aircraft, tire, and runway parameters. Several promising means are described for improving tire/runway water drainage capability, brake system efficiency, and pilot training to help optimize aircraft traction performance on wet runways

A synthesis of fuzzy rule-based system verification.

The verification of fuzzy rule bases for anomalies has received increasing attention these last few years. Many different approaches have been suggested and many are still under investigation. In this paper, we give a synthesis of methods proposed in literature that try to extend the verification of clasical rule bases to the case of fuzzy knowledge modelling, without needing a set of representative input. Within this area of fyzzy V&V we identify two dual lines of thought respectively leading to what is identified as static and dynamic anomaly detection methods. Static anomaly detection essentially tries to use similarity, affinity or matching measures to identify anomalies wihin a fuzzy rule base. It is assumed that the detection methods can be the same as those used in a non-fuzzy environment, except that the formerly mentioned measures indicate the degree of matching of two fuzzy expressions. Dynamic anomaly detection starts from the basic idea that any anomaly within a knowledge representation formalism, i.c. fuzzy if-then rules, can be identified by performing a dynamic analysis of the knowledge system, even without providing special input to the system. By imposing a constraint on the results of inference for an anomaly not to occur, one creates definitions of the anomalies that can only be verified if the inference pocess, and thereby the fuzzy inference operator is involved in the analysis. The major outcome of the confrontation between both approaches is that their results, stated in terms of necessary and/or sufficient conditions for anomaly detection within a particular situation, are difficult to reconcile. The duality between approaces seems to have translated into a duality in results. This article addresses precisely this issue by presenting a theoretical framework which anables us to effectively evaluate the results of both static and dynamic verification theories.

The behavior of fuzzy implications in a fuzzy knowledge base.

More and more companies today discover the advantages of using knowledge bases for their processes and services. Recently, fuzzy set theory has also captured the attention due to good performances within control systems. Therefore, it is very appealing to combine the advantages of these two areas into a fuzzy knowledge base. However, obtaining the results of control systems in a knowleg based environment is not so straightforward. This paper will investigate one aspect of the reasoning process, namely the behavior of the implication. From the different tests performed, four main behaviors of implications can be found. First of all, there are the implications not always resulting in a convex set. A second classs - the so-called impotent implications- doesn't change the predefined set at all. A third grouping reveals always a constant value portion, that rises or falls according to the changed input. A final divsion shifts the complete set in its whole conformably the intuition.Implications; Companies; Advantages; Knowledge; Processes; Theory; Performance; Systems; Value;