Kumar and Billinton have presented a new technique for obtaining the steady-state probabilities from a flow graph based on Markov model. By examining the graph and choosing suitable input and output nodes, the steady-state probabilities can be obtained directly by using the flow graph. In this paper this graphical technique is applied for a k-out-of-n: G repairable system. Consequently a new derivation way of the formulae for the steady-state availability and MTBF is obtained.

Various applications of cellular neural network (CNN) are reported such as a feature extraction of the patterns, an extraction of the edges or corners of a figure, noise exclusion, searching in maze and so forth. In this paper, we propose a cellular neural network whose each cell has more than two output levels. By using the output function which has several saturated levels, each cell turns to have several output states. The multiple-valued CNN enhances its associative memory function so as to express various kinds of aspects. We report an application of the enhanced asscociative memory function to a diagnosis of the liver troubles.