No abstract

This paper describes about cutting stock scheduling system using simulated annealing method. The cutting stock problem is that of combinatorial optimization. The following functions are needed for the cutting stock scheduling system. 1) Balance adjusting function for productivity, yield rate and delivery time. 2) The results of scheduling have to satisfy constraint condition of coil cutting machine. 3) There is time limitation for making cutting stock schedule. To overcome these problem, we realize new cutting stock scheduling system through following methods. 1) Weights for each evaluated item are introduced and sum of these items are minimized by simulated annealing. 2) The new neighborhood structure is developed for this problem which can satisfy constraint condition of coil cutting machine. 3) The adjusting parameter for calculation time is newly introduced for Huang's annealing schedule, so that we can select best solution under the limit of calculation time. This cutting stock scheduling system is applied to plate plant and used as one of production planning system.

In a pmctieal image processing such Wavelet Tmnsfonn (WT), the function orthogonality is required for reconstruction of the original image. The orthogonality has disadvantage that the selected filter is not necessarily optimal from a viewpoint from human retinal realization. It is not necessary to select an orthogonal templates in Cellular Neuml Network (GNN) image processing, because the CNN is non-linear analog circuit to obtain equilibrium points automatically and simultaneously. This paper describes CNN image compression and reconstruction based on a non-orthogonal WT. This system have an advantage of non-dependency of image scanning by spatio-tempoml GNN dynamics. It is very important that the rewnstruction of tmnsmitted compression image is done simultaneously by parallel neurons based on the "regularization" of ill-posed problem which is caused in a retinal system of a human bmin.