This paper deals with the problem of connecting two planer shapes on a single boundary. This jigsaw-like problem is encountered in many fields. The previous approaches involved what is referred to as pattern matching technology, including feature extraction. Our algorithm takes a different approach, wherein we evaluate the connection of two shapes by the area remaining between them. In this algorithm, we attempted to overlap matching boundaries of two shapes, so as to minimize the region lying between them. If this region was, in fact, narrow enough, it would prove their near-perfect connection. The advantage of this particular method is that it requires no complicated feature extractions or no point-to-point correspondence between boundaries. In order to put this method to practical use, we presented an algorithm to calculate the aforementioned area regardless of shape. A computer simulation of the connection using Koch's curves as boundaries shows its efficiency.

It is an important topic to express solid objects in CAD, CAM and CG with a uniform way. In this article, a constructive dispersed surface points-set model (CDSPM) is proposed. CDSPM is made up by extracting the dispersed surface points sets from the simpler shapes, and then compose the points sets through a sequence of new defined Boolean set operation rules. Using these rules, solid object can be defined step by step, and the redundant data can be easily eliminated. In the article, a novel rendering method using the CDSPM medel data is introduced. A re-projection and pixel interpolation method enable us to effectively generate a reasonable image from a set of discrete surface points.

Neural network pruning is a technique to obtain a fully functional subset of a redundant network for the efficiency of computation. A new method to prune a redundant three-layered neural network by means of neural element fusion" is introduced. In contrast to conventional pruning techniques that remove unimportant portions of the network, our method fuses a pair of hidden layer units so that features accumulated in both units are preserved as possible. The pair of hidden layer units to be fused is chosen by evaluating a firing similarity. This similarity measure also informs when the pruning should be stopped. The fusing method was compared with well known unit removing" methods on computer simulations. The results show that our fusing method considerably reduces the error increase due to the pruning, even in subminimal networks where conventional methods are ineffective. This enables to cut down the total cost of computation to reach the minmal network configuration.