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A novel cobalt recovering process from a cobalt-based cathode electrode in a lithium secondary cell was developed using a hydrothermal treatment combined with a pyrometallurgical technique. A cobalt-based cathode electrode was prepared by casting the mixture of LiCoO 2 , poly(vinylidene fluoride) and conductive carbon black onto an Al foil, and this model electrode was used as a test sample. A hydrothermal treatment of the cathode sample was carried out using pure water as a solvent in the temperature range of 423-473 K for the duration of 0-40 h. The hydrothermal treatment at 473 K for more than 15 h led the cathode sample to the disintegration into powdery particles. By the hydrothermal treatment under the optimum condition at 473 K for 20 h, more than 99.9 mass% of Co and 98 mass% of Al in the cathode sample was reclaimed as a form of spinel type Co(Co x Al 2-x )O 4 (0

This study presents a new architecture of neural networks named "cubic neural network (CNN)", which possesses multi-levels-of-information processing and has an ability of parallel distributed signal processing as an intelligent control method. Each level of this CNN processes different degree of abstracted signals and it enables adaptation for abnormal state. In this paper, the fundamental construction, the learning method and the abstraction method of CNN are described. And the control ability of this method was verified by the experimental results of inverted pendulum which has a large change of parameters.

Neural network control has been applied to various objects, and showed its validity. From the viewpoint of intelligent control, however, it is considered that most of utilizations of neural network are no more than a technique in which a part of information processing is replaced with that using a neural network and they are not able to cope with abnormal situation. This study introduces a new concept of hierarchical information abstraction. This study presents a new technique of intelligent control using the neural network, called "Cubic Neural Network" (CNN) which possesses multi-levels of information abstraction, and furthermore presents a systematic synthesis method of CNN. Therefore, qualitative knowledge or information can be used in addition to ordinary feedback control using only quantitative measured values. And a robust control which has large applicable regions is realized. And, as an example, the control of inverted pendulum including supposed or not supposed large parameter change is dealt with, and the usefulness of the present method is shown.