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It is very difficult for nuclear power plant operators to predict and identify the major severe accident scenarios following an initiating event by staring at temporal trends of important parameters. In this regard, a probabilistic neural network (PNN) that has been applied well to the classification problems is used in order to classify accidents into groups of initiating events such as loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR). Also, a fuzzy neural network (FNN) is designed to identify their major severe accident scenarios after the initiating events. The inputs to PNN and FNN are initial time-integrated values obtained by integrating measurement signals during a short time interval after reactor scram. An automatic structure constructor for the fuzzy neural network automatically selects the input variables from the time-integrated values of many measured signals, and optimizes the number of rules and its related parameters. In cases that an initiating event develops into a severe accident, this may happen when plant operators do not follow the appropriate accident management guidance or plant safety systems do not work, the proposed algorithm showed accurate classification of initiating events. Also, it well predicted timings for important occurrences during severe accident progression scenarios, which is very helpful to perform severe accident management. [ABSTRACT FROM AUTHOR]

In this paper, a new rehabilitation training system was developed to improve equilibrium sense control by combining virtual reality technology with a fixed exercise bicycle. In order to evaluate the effectiveness of the training system, twenty normal healthy adults participated as test subjects in the investigation of the influence of different the parameters on equilibrium sensory control. Several factors including path deviation, cycling time, cycling velocity, center of pressure were evaluated and analyzed quantitatively. Also, we investigated the usefulness of virtual feedback information for weight shift on the improvement of the effect of equilibrium training. After conducting repeated tests, we found improvements not only in of the ability to controlling balance and weight shift but also in the overall cycling capabilities such as cycling speed. Therefore, the new training system utilizing an excise bike and a virtual reality system is found to be effective on the improvement of equilibrium sense, and the technology we have developed seems to have a good prospect in the rehabilitation field.