Evaluation is an important and basic issue for software dependability. However, traditional researches mostly focused on the conceptions and properties of dependability, which resulted in lots of different views about software dependability. Based upon Pi Calculus, dependable action path of software and real action path are set up through series of actions, which is decomposed by running software behaviors. In addition, three kinds of relationships between behaviors are defined. Consequently, the dependability is calculated by comparing the real running software actions to the decomposed ones based on the defined relationships. Experimental result conducted by MWB shows that the proposed method in one behavior is reasonable. At last, a real experiment proves the proposed measure method is more valid than traditional one.
Both fuzzy c-means (FCM) clustering and outlier detection are useful data mining techniques in real applications. In this paper, we show that the task of outlier detection could be achieved as by-product of fuzzy c-means clustering. The proposed strategy consists of two stages. The first stage consists of purely fuzzy c-means process, while the second stage identifies exceptional objects according to a novel metric based on the entropy of membership values. We provide experimental results to demonstrate the effectiveness of our technique.
At present, lots of work focus on spectrum allocation of wireless networks, and pay little attention about optimization network access. In this paper, we propose a cognitive access selection approach of heterogeneous wireless networks based on fuzzy logic. By means of established access selection factors and its membership functions(MFs), we can access optimal network using presupposition fuzzy rules according to service types. Finally, simulation experiments shown the approach is effective.
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