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This paper describes a nonlinear disaggregation technique for the operation of multireservoir systems. The disaggregation is done by training a neural network to give, for an aggregated storage level, the storage level of each reservoir of the system. The training set is obtained by solving the deterministic operating problem of a large number of equally likely flow sequences. The training is achieved using the back propagation method, and the minimization of the quadratic error is computed by a variable step gradient method. The aggregated storage level can be determined by stochastic dynamic programming in which all hydroelectric installations are aggregated to form one equivalent reservoir. The results of applying the learning disaggregation technique to Quebec's La Grande river are reported, and a comparison with the principal component analysis disaggregation technique is given.

Five-bar planar parallel robots for pick and place operations are always designed so that their singularity loci are significantly reduced. In these robots, the length of the proximal links is different from the length of the distal links. As a consequence, the workspace of the robot is significantly limited, since there are holes in it. In contrast, we propose a design in which all four links have equal lengths. Since such a design leads to more parallel singularities, a strategy for avoiding them by switching working modes is proposed. As a result, the usable workspace of the robot is significantly increased. The idea has been implemented on an industrial-grade prototype and the latter is described in detail.

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